Overview

Dataset statistics

Number of variables231
Number of observations1460
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory668.8 KiB
Average record size in memory469.1 B

Variable types

Numeric26
Categorical205

Alerts

MSSubClass is highly overall correlated with HalfBath and 9 other fieldsHigh correlation
LotFrontage is highly overall correlated with LotAreaHigh correlation
LotArea is highly overall correlated with LotFrontageHigh correlation
OverallQual is highly overall correlated with YearBuilt and 14 other fieldsHigh correlation
OverallCond is highly overall correlated with Foundation_PConc and 1 other fieldsHigh correlation
YearBuilt is highly overall correlated with OverallQual and 23 other fieldsHigh correlation
YearRemodAdd is highly overall correlated with OverallQual and 15 other fieldsHigh correlation
BsmtFinSF1 is highly overall correlated with BsmtUnfSFHigh correlation
BsmtFinSF2 is highly overall correlated with BsmtFinType2_Rec and 1 other fieldsHigh correlation
BsmtUnfSF is highly overall correlated with BsmtFinSF1 and 1 other fieldsHigh correlation
TotalBsmtSF is highly overall correlated with 1stFlrSFHigh correlation
1stFlrSF is highly overall correlated with TotalBsmtSFHigh correlation
2ndFlrSF is highly overall correlated with GrLivArea and 5 other fieldsHigh correlation
LowQualFinSF is highly overall correlated with HouseStyle_2.5Fin and 1 other fieldsHigh correlation
GrLivArea is highly overall correlated with OverallQual and 3 other fieldsHigh correlation
BedroomAbvGr is highly overall correlated with 2ndFlrSF and 2 other fieldsHigh correlation
TotRmsAbvGrd is highly overall correlated with 2ndFlrSF and 2 other fieldsHigh correlation
GarageYrBlt is highly overall correlated with OverallQual and 18 other fieldsHigh correlation
GarageArea is highly overall correlated with OverallQual and 5 other fieldsHigh correlation
3SsnPorch is highly overall correlated with Foundation_WoodHigh correlation
FullBath is highly overall correlated with BsmtQual_TAHigh correlation
HalfBath is highly overall correlated with MSSubClass and 1 other fieldsHigh correlation
KitchenAbvGr is highly overall correlated with BldgType_Duplex and 1 other fieldsHigh correlation
GarageCars is highly overall correlated with GarageArea and 4 other fieldsHigh correlation
MSZoning_FV is highly overall correlated with Neighborhood_SomerstHigh correlation
MSZoning_RL is highly overall correlated with MSZoning_RMHigh correlation
MSZoning_RM is highly overall correlated with MSZoning_RL and 1 other fieldsHigh correlation
LandContour_Bnk is highly overall correlated with LandContour_LvlHigh correlation
LandContour_HLS is highly overall correlated with LandContour_LvlHigh correlation
LandContour_Lvl is highly overall correlated with LandContour_Bnk and 1 other fieldsHigh correlation
LotConfig_Corner is highly overall correlated with LotConfig_InsideHigh correlation
LotConfig_Inside is highly overall correlated with LotConfig_CornerHigh correlation
Neighborhood_MeadowV is highly overall correlated with Exterior1st_CemntBd and 1 other fieldsHigh correlation
Neighborhood_NAmes is highly overall correlated with YearBuiltHigh correlation
Neighborhood_NPkVill is highly overall correlated with Exterior2nd_Brk CmnHigh correlation
Neighborhood_OldTown is highly overall correlated with YearBuilt and 1 other fieldsHigh correlation
Neighborhood_Somerst is highly overall correlated with MSZoning_FVHigh correlation
BldgType_1Fam is highly overall correlated with MSSubClass and 1 other fieldsHigh correlation
BldgType_2fmCon is highly overall correlated with MSSubClassHigh correlation
BldgType_Duplex is highly overall correlated with MSSubClass and 1 other fieldsHigh correlation
BldgType_Twnhs is highly overall correlated with MSSubClassHigh correlation
BldgType_TwnhsE is highly overall correlated with MSSubClass and 1 other fieldsHigh correlation
HouseStyle_1.5Fin is highly overall correlated with MSSubClass and 2 other fieldsHigh correlation
HouseStyle_1Story is highly overall correlated with MSSubClass and 2 other fieldsHigh correlation
HouseStyle_2.5Fin is highly overall correlated with LowQualFinSFHigh correlation
HouseStyle_2Story is highly overall correlated with MSSubClass and 3 other fieldsHigh correlation
HouseStyle_SLvl is highly overall correlated with MSSubClassHigh correlation
RoofStyle_Gable is highly overall correlated with RoofStyle_HipHigh correlation
RoofStyle_Hip is highly overall correlated with RoofStyle_GableHigh correlation
Exterior1st_AsbShng is highly overall correlated with Exterior2nd_AsbShngHigh correlation
Exterior1st_BrkFace is highly overall correlated with Exterior2nd_BrkFaceHigh correlation
Exterior1st_CemntBd is highly overall correlated with Neighborhood_MeadowV and 1 other fieldsHigh correlation
Exterior1st_HdBoard is highly overall correlated with Exterior2nd_HdBoardHigh correlation
Exterior1st_MetalSd is highly overall correlated with Exterior2nd_MetalSdHigh correlation
Exterior1st_Plywood is highly overall correlated with Exterior2nd_PlywoodHigh correlation
Exterior1st_Stucco is highly overall correlated with Exterior2nd_StuccoHigh correlation
Exterior1st_VinylSd is highly overall correlated with YearBuilt and 4 other fieldsHigh correlation
Exterior1st_Wd Sdng is highly overall correlated with Exterior2nd_Wd SdngHigh correlation
Exterior1st_WdShing is highly overall correlated with Exterior2nd_Wd ShngHigh correlation
Exterior2nd_AsbShng is highly overall correlated with Exterior1st_AsbShngHigh correlation
Exterior2nd_Brk Cmn is highly overall correlated with Neighborhood_NPkVillHigh correlation
Exterior2nd_BrkFace is highly overall correlated with Exterior1st_BrkFaceHigh correlation
Exterior2nd_CmentBd is highly overall correlated with Neighborhood_MeadowV and 1 other fieldsHigh correlation
Exterior2nd_HdBoard is highly overall correlated with Exterior1st_HdBoardHigh correlation
Exterior2nd_MetalSd is highly overall correlated with Exterior1st_MetalSdHigh correlation
Exterior2nd_Plywood is highly overall correlated with Exterior1st_PlywoodHigh correlation
Exterior2nd_Stucco is highly overall correlated with Exterior1st_StuccoHigh correlation
Exterior2nd_VinylSd is highly overall correlated with YearBuilt and 4 other fieldsHigh correlation
Exterior2nd_Wd Sdng is highly overall correlated with Exterior1st_Wd SdngHigh correlation
Exterior2nd_Wd Shng is highly overall correlated with Exterior1st_WdShingHigh correlation
MasVnrType_BrkFace is highly overall correlated with MasVnrType_NoneHigh correlation
MasVnrType_None is highly overall correlated with MasVnrType_BrkFaceHigh correlation
ExterQual_Ex is highly overall correlated with OverallQual and 2 other fieldsHigh correlation
ExterQual_Gd is highly overall correlated with OverallQual and 8 other fieldsHigh correlation
ExterQual_TA is highly overall correlated with OverallQual and 10 other fieldsHigh correlation
ExterCond_Gd is highly overall correlated with ExterCond_TAHigh correlation
ExterCond_TA is highly overall correlated with ExterCond_GdHigh correlation
Foundation_BrkTil is highly overall correlated with YearBuilt and 1 other fieldsHigh correlation
Foundation_CBlock is highly overall correlated with YearBuilt and 4 other fieldsHigh correlation
Foundation_PConc is highly overall correlated with OverallQual and 12 other fieldsHigh correlation
Foundation_Wood is highly overall correlated with 3SsnPorchHigh correlation
BsmtQual_Ex is highly overall correlated with OverallQual and 2 other fieldsHigh correlation
BsmtQual_Gd is highly overall correlated with OverallQual and 3 other fieldsHigh correlation
BsmtQual_TA is highly overall correlated with OverallQual and 10 other fieldsHigh correlation
BsmtCond_Fa is highly overall correlated with BsmtCond_TAHigh correlation
BsmtCond_Gd is highly overall correlated with BsmtCond_TAHigh correlation
BsmtCond_Po is highly overall correlated with OverallCondHigh correlation
BsmtCond_TA is highly overall correlated with BsmtCond_Fa and 1 other fieldsHigh correlation
BsmtExposure_Av is highly overall correlated with BsmtExposure_NoHigh correlation
BsmtExposure_No is highly overall correlated with BsmtExposure_AvHigh correlation
BsmtFinType1_GLQ is highly overall correlated with YearBuiltHigh correlation
BsmtFinType1_Unf is highly overall correlated with BsmtUnfSFHigh correlation
BsmtFinType2_Rec is highly overall correlated with BsmtFinSF2High correlation
BsmtFinType2_Unf is highly overall correlated with BsmtFinSF2High correlation
Heating_GasA is highly overall correlated with Heating_GasWHigh correlation
Heating_GasW is highly overall correlated with Heating_GasAHigh correlation
Heating_OthW is highly overall correlated with LowQualFinSFHigh correlation
HeatingQC_Ex is highly overall correlated with YearBuilt and 5 other fieldsHigh correlation
HeatingQC_TA is highly overall correlated with YearBuilt and 2 other fieldsHigh correlation
CentralAir_N is highly overall correlated with CentralAir_YHigh correlation
CentralAir_Y is highly overall correlated with CentralAir_NHigh correlation
Electrical_FuseA is highly overall correlated with Electrical_SBrkrHigh correlation
Electrical_SBrkr is highly overall correlated with Electrical_FuseAHigh correlation
KitchenQual_Ex is highly overall correlated with OverallQual and 2 other fieldsHigh correlation
KitchenQual_Gd is highly overall correlated with OverallQual and 6 other fieldsHigh correlation
KitchenQual_TA is highly overall correlated with OverallQual and 7 other fieldsHigh correlation
Functional_Min1 is highly overall correlated with Functional_TypHigh correlation
Functional_Min2 is highly overall correlated with Functional_TypHigh correlation
Functional_Typ is highly overall correlated with Functional_Min1 and 1 other fieldsHigh correlation
GarageType_Attchd is highly overall correlated with YearBuilt and 1 other fieldsHigh correlation
GarageType_Detchd is highly overall correlated with YearBuilt and 2 other fieldsHigh correlation
GarageFinish_RFn is highly overall correlated with GarageFinish_UnfHigh correlation
GarageFinish_Unf is highly overall correlated with YearBuilt and 3 other fieldsHigh correlation
GarageQual_Ex is highly overall correlated with GarageCond_ExHigh correlation
GarageQual_Fa is highly overall correlated with GarageQual_TAHigh correlation
GarageQual_Po is highly overall correlated with GarageCond_PoHigh correlation
GarageQual_TA is highly overall correlated with GarageYrBlt and 4 other fieldsHigh correlation
GarageCond_Ex is highly overall correlated with GarageQual_ExHigh correlation
GarageCond_Po is highly overall correlated with GarageQual_PoHigh correlation
GarageCond_TA is highly overall correlated with GarageYrBlt and 3 other fieldsHigh correlation
SaleType_New is highly overall correlated with YearRemodAdd and 3 other fieldsHigh correlation
SaleType_WD is highly overall correlated with SaleType_New and 2 other fieldsHigh correlation
SaleCondition_Abnorml is highly overall correlated with SaleCondition_NormalHigh correlation
SaleCondition_AdjLand is highly overall correlated with KitchenAbvGrHigh correlation
SaleCondition_Normal is highly overall correlated with SaleType_New and 3 other fieldsHigh correlation
SaleCondition_Partial is highly overall correlated with YearRemodAdd and 3 other fieldsHigh correlation
LotShape1_Irr is highly overall correlated with LotShape1_RegHigh correlation
LotShape1_Reg is highly overall correlated with LotShape1_IrrHigh correlation
BsmtHalfBath is highly imbalanced (79.7%)Imbalance
KitchenAbvGr is highly imbalanced (85.7%)Imbalance
MSZoning_C (all) is highly imbalanced (94.1%)Imbalance
MSZoning_FV is highly imbalanced (73.7%)Imbalance
MSZoning_RH is highly imbalanced (91.3%)Imbalance
LandContour_Bnk is highly imbalanced (74.3%)Imbalance
LandContour_HLS is highly imbalanced (78.5%)Imbalance
LandContour_Low is highly imbalanced (83.3%)Imbalance
LandContour_Lvl is highly imbalanced (52.5%)Imbalance
LotConfig_CulDSac is highly imbalanced (65.5%)Imbalance
LotConfig_FR2 is highly imbalanced (79.5%)Imbalance
LotConfig_FR3 is highly imbalanced (97.3%)Imbalance
Neighborhood_Blmngtn is highly imbalanced (90.8%)Imbalance
Neighborhood_Blueste is highly imbalanced (98.5%)Imbalance
Neighborhood_BrDale is highly imbalanced (91.3%)Imbalance
Neighborhood_BrkSide is highly imbalanced (75.9%)Imbalance
Neighborhood_ClearCr is highly imbalanced (86.3%)Imbalance
Neighborhood_CollgCr is highly imbalanced (52.2%)Imbalance
Neighborhood_Crawfor is highly imbalanced (78.1%)Imbalance
Neighborhood_Edwards is highly imbalanced (64.0%)Imbalance
Neighborhood_Gilbert is highly imbalanced (69.6%)Imbalance
Neighborhood_IDOTRR is highly imbalanced (83.0%)Imbalance
Neighborhood_MeadowV is highly imbalanced (90.8%)Imbalance
Neighborhood_Mitchel is highly imbalanced (78.8%)Imbalance
Neighborhood_NPkVill is highly imbalanced (94.6%)Imbalance
Neighborhood_NWAmes is highly imbalanced (71.4%)Imbalance
Neighborhood_NoRidge is highly imbalanced (81.5%)Imbalance
Neighborhood_NridgHt is highly imbalanced (70.2%)Imbalance
Neighborhood_OldTown is highly imbalanced (60.7%)Imbalance
Neighborhood_SWISU is highly imbalanced (87.5%)Imbalance
Neighborhood_Sawyer is highly imbalanced (71.1%)Imbalance
Neighborhood_SawyerW is highly imbalanced (75.6%)Imbalance
Neighborhood_Somerst is highly imbalanced (67.7%)Imbalance
Neighborhood_StoneBr is highly imbalanced (87.5%)Imbalance
Neighborhood_Timber is highly imbalanced (82.6%)Imbalance
Neighborhood_Veenker is highly imbalanced (93.6%)Imbalance
BldgType_2fmCon is highly imbalanced (85.2%)Imbalance
BldgType_Duplex is highly imbalanced (77.8%)Imbalance
BldgType_Twnhs is highly imbalanced (80.8%)Imbalance
BldgType_TwnhsE is highly imbalanced (60.5%)Imbalance
HouseStyle_1.5Fin is highly imbalanced (51.4%)Imbalance
HouseStyle_1.5Unf is highly imbalanced (92.2%)Imbalance
HouseStyle_2.5Fin is highly imbalanced (95.1%)Imbalance
HouseStyle_2.5Unf is highly imbalanced (93.6%)Imbalance
HouseStyle_SFoyer is highly imbalanced (83.0%)Imbalance
HouseStyle_SLvl is highly imbalanced (73.7%)Imbalance
RoofStyle_Flat is highly imbalanced (92.7%)Imbalance
RoofStyle_Gambrel is highly imbalanced (93.6%)Imbalance
RoofStyle_Mansard is highly imbalanced (95.6%)Imbalance
RoofStyle_Shed is highly imbalanced (98.5%)Imbalance
Exterior1st_AsbShng is highly imbalanced (89.6%)Imbalance
Exterior1st_AsphShn is highly imbalanced (99.2%)Imbalance
Exterior1st_BrkComm is highly imbalanced (98.5%)Imbalance
Exterior1st_BrkFace is highly imbalanced (78.5%)Imbalance
Exterior1st_CBlock is highly imbalanced (99.2%)Imbalance
Exterior1st_CemntBd is highly imbalanced (75.0%)Imbalance
Exterior1st_ImStucc is highly imbalanced (99.2%)Imbalance
Exterior1st_Plywood is highly imbalanced (61.9%)Imbalance
Exterior1st_Stone is highly imbalanced (98.5%)Imbalance
Exterior1st_Stucco is highly imbalanced (87.5%)Imbalance
Exterior1st_WdShing is highly imbalanced (87.1%)Imbalance
Exterior2nd_AsbShng is highly imbalanced (89.6%)Imbalance
Exterior2nd_AsphShn is highly imbalanced (97.9%)Imbalance
Exterior2nd_Brk Cmn is highly imbalanced (95.6%)Imbalance
Exterior2nd_BrkFace is highly imbalanced (87.5%)Imbalance
Exterior2nd_CBlock is highly imbalanced (99.2%)Imbalance
Exterior2nd_CmentBd is highly imbalanced (75.3%)Imbalance
Exterior2nd_ImStucc is highly imbalanced (94.1%)Imbalance
Exterior2nd_Other is highly imbalanced (99.2%)Imbalance
Exterior2nd_Plywood is highly imbalanced (54.0%)Imbalance
Exterior2nd_Stone is highly imbalanced (96.7%)Imbalance
Exterior2nd_Stucco is highly imbalanced (87.1%)Imbalance
Exterior2nd_Wd Shng is highly imbalanced (82.6%)Imbalance
MasVnrType_BrkCmn is highly imbalanced (91.7%)Imbalance
MasVnrType_Stone is highly imbalanced (57.1%)Imbalance
ExterQual_Ex is highly imbalanced (77.8%)Imbalance
ExterQual_Fa is highly imbalanced (92.2%)Imbalance
ExterCond_Ex is highly imbalanced (97.9%)Imbalance
ExterCond_Fa is highly imbalanced (86.3%)Imbalance
ExterCond_Gd is highly imbalanced (53.1%)Imbalance
ExterCond_Po is highly imbalanced (99.2%)Imbalance
Foundation_BrkTil is highly imbalanced (53.1%)Imbalance
Foundation_Slab is highly imbalanced (87.9%)Imbalance
Foundation_Stone is highly imbalanced (96.2%)Imbalance
Foundation_Wood is highly imbalanced (97.9%)Imbalance
BsmtQual_Ex is highly imbalanced (58.8%)Imbalance
BsmtQual_Fa is highly imbalanced (83.7%)Imbalance
BsmtCond_Fa is highly imbalanced (80.2%)Imbalance
BsmtCond_Gd is highly imbalanced (73.7%)Imbalance
BsmtCond_Po is highly imbalanced (98.5%)Imbalance
BsmtCond_TA is highly imbalanced (52.5%)Imbalance
BsmtExposure_Gd is highly imbalanced (55.8%)Imbalance
BsmtExposure_Mn is highly imbalanced (60.5%)Imbalance
BsmtFinType1_BLQ is highly imbalanced (52.7%)Imbalance
BsmtFinType1_LwQ is highly imbalanced (71.1%)Imbalance
BsmtFinType1_Rec is highly imbalanced (56.0%)Imbalance
BsmtFinType2_ALQ is highly imbalanced (90.0%)Imbalance
BsmtFinType2_BLQ is highly imbalanced (84.4%)Imbalance
BsmtFinType2_GLQ is highly imbalanced (92.2%)Imbalance
BsmtFinType2_LwQ is highly imbalanced (79.8%)Imbalance
BsmtFinType2_Rec is highly imbalanced (77.2%)Imbalance
Heating_Floor is highly imbalanced (99.2%)Imbalance
Heating_GasA is highly imbalanced (84.8%)Imbalance
Heating_GasW is highly imbalanced (90.4%)Imbalance
Heating_Grav is highly imbalanced (95.6%)Imbalance
Heating_OthW is highly imbalanced (98.5%)Imbalance
Heating_Wall is highly imbalanced (97.3%)Imbalance
HeatingQC_Fa is highly imbalanced (78.8%)Imbalance
HeatingQC_Po is highly imbalanced (99.2%)Imbalance
CentralAir_N is highly imbalanced (65.3%)Imbalance
CentralAir_Y is highly imbalanced (65.3%)Imbalance
Electrical_FuseA is highly imbalanced (65.5%)Imbalance
Electrical_FuseF is highly imbalanced (86.7%)Imbalance
Electrical_FuseP is highly imbalanced (97.9%)Imbalance
Electrical_Mix is highly imbalanced (99.2%)Imbalance
Electrical_SBrkr is highly imbalanced (57.6%)Imbalance
KitchenQual_Ex is highly imbalanced (64.0%)Imbalance
KitchenQual_Fa is highly imbalanced (82.2%)Imbalance
Functional_Maj1 is highly imbalanced (92.2%)Imbalance
Functional_Maj2 is highly imbalanced (96.7%)Imbalance
Functional_Min1 is highly imbalanced (85.2%)Imbalance
Functional_Min2 is highly imbalanced (84.0%)Imbalance
Functional_Mod is highly imbalanced (91.7%)Imbalance
Functional_Sev is highly imbalanced (99.2%)Imbalance
Functional_Typ is highly imbalanced (64.0%)Imbalance
GarageType_2Types is highly imbalanced (96.2%)Imbalance
GarageType_Basment is highly imbalanced (90.0%)Imbalance
GarageType_BuiltIn is highly imbalanced (67.1%)Imbalance
GarageType_CarPort is highly imbalanced (94.6%)Imbalance
GarageQual_Ex is highly imbalanced (97.9%)Imbalance
GarageQual_Fa is highly imbalanced (79.1%)Imbalance
GarageQual_Gd is highly imbalanced (92.2%)Imbalance
GarageQual_Po is highly imbalanced (97.9%)Imbalance
GarageQual_TA is highly imbalanced (52.5%)Imbalance
GarageCond_Ex is highly imbalanced (98.5%)Imbalance
GarageCond_Fa is highly imbalanced (83.7%)Imbalance
GarageCond_Gd is highly imbalanced (94.6%)Imbalance
GarageCond_Po is highly imbalanced (95.6%)Imbalance
GarageCond_TA is highly imbalanced (55.8%)Imbalance
SaleType_COD is highly imbalanced (80.8%)Imbalance
SaleType_CWD is highly imbalanced (97.3%)Imbalance
SaleType_Con is highly imbalanced (98.5%)Imbalance
SaleType_ConLD is highly imbalanced (94.6%)Imbalance
SaleType_ConLI is highly imbalanced (96.7%)Imbalance
SaleType_ConLw is highly imbalanced (96.7%)Imbalance
SaleType_New is highly imbalanced (58.5%)Imbalance
SaleType_Oth is highly imbalanced (97.9%)Imbalance
SaleCondition_Abnorml is highly imbalanced (63.7%)Imbalance
SaleCondition_AdjLand is highly imbalanced (97.3%)Imbalance
SaleCondition_Alloca is highly imbalanced (93.1%)Imbalance
SaleCondition_Family is highly imbalanced (89.6%)Imbalance
SaleCondition_Partial is highly imbalanced (57.8%)Imbalance
BsmtFinSF1 has 467 (32.0%) zerosZeros
BsmtFinSF2 has 1293 (88.6%) zerosZeros
BsmtUnfSF has 118 (8.1%) zerosZeros
TotalBsmtSF has 37 (2.5%) zerosZeros
2ndFlrSF has 829 (56.8%) zerosZeros
LowQualFinSF has 1434 (98.2%) zerosZeros
GarageArea has 81 (5.5%) zerosZeros
WoodDeckSF has 761 (52.1%) zerosZeros
OpenPorchSF has 656 (44.9%) zerosZeros
EnclosedPorch has 1252 (85.8%) zerosZeros
3SsnPorch has 1436 (98.4%) zerosZeros
ScreenPorch has 1344 (92.1%) zerosZeros
PoolArea has 1453 (99.5%) zerosZeros

Reproduction

Analysis started2023-05-18 13:04:33.480143
Analysis finished2023-05-18 13:06:14.369338
Duration1 minute and 40.89 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

MSSubClass
Real number (ℝ)

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.89726
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:14.398664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.300571
Coefficient of variation (CV)0.74345532
Kurtosis1.580188
Mean56.89726
Median Absolute Deviation (MAD)30
Skewness1.4076567
Sum83070
Variance1789.3383
MonotonicityNot monotonic
2023-05-18T15:06:14.443497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20 536
36.7%
60 299
20.5%
50 144
 
9.9%
120 87
 
6.0%
30 69
 
4.7%
160 63
 
4.3%
70 60
 
4.1%
80 58
 
4.0%
90 52
 
3.6%
190 30
 
2.1%
Other values (5) 62
 
4.2%
ValueCountFrequency (%)
20 536
36.7%
30 69
 
4.7%
40 4
 
0.3%
45 12
 
0.8%
50 144
 
9.9%
60 299
20.5%
70 60
 
4.1%
75 16
 
1.1%
80 58
 
4.0%
85 20
 
1.4%
ValueCountFrequency (%)
190 30
 
2.1%
180 10
 
0.7%
160 63
 
4.3%
120 87
 
6.0%
90 52
 
3.6%
85 20
 
1.4%
80 58
 
4.0%
75 16
 
1.1%
70 60
 
4.1%
60 299
20.5%

LotFrontage
Real number (ℝ)

Distinct111
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.049958
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:14.500343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile35.95
Q160
median70.049958
Q379
95-th percentile104
Maximum313
Range292
Interquartile range (IQR)19

Descriptive statistics

Standard deviation22.024023
Coefficient of variation (CV)0.31440451
Kurtosis21.848165
Mean70.049958
Median Absolute Deviation (MAD)10.049958
Skewness2.3849502
Sum102272.94
Variance485.05758
MonotonicityNot monotonic
2023-05-18T15:06:14.555099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.04995837 259
 
17.7%
60 143
 
9.8%
70 70
 
4.8%
80 69
 
4.7%
50 57
 
3.9%
75 53
 
3.6%
65 44
 
3.0%
85 40
 
2.7%
78 25
 
1.7%
90 23
 
1.6%
Other values (101) 677
46.4%
ValueCountFrequency (%)
21 23
1.6%
24 19
1.3%
30 6
 
0.4%
32 5
 
0.3%
33 1
 
0.1%
34 10
0.7%
35 9
 
0.6%
36 6
 
0.4%
37 5
 
0.3%
38 1
 
0.1%
ValueCountFrequency (%)
313 2
0.1%
182 1
0.1%
174 2
0.1%
168 1
0.1%
160 1
0.1%
153 1
0.1%
152 1
0.1%
150 1
0.1%
149 1
0.1%
144 1
0.1%

LotArea
Real number (ℝ)

Distinct1073
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10516.828
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:14.616791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3311.7
Q17553.5
median9478.5
Q311601.5
95-th percentile17401.15
Maximum215245
Range213945
Interquartile range (IQR)4048

Descriptive statistics

Standard deviation9981.2649
Coefficient of variation (CV)0.9490756
Kurtosis203.24327
Mean10516.828
Median Absolute Deviation (MAD)1998
Skewness12.207688
Sum15354569
Variance99625650
MonotonicityNot monotonic
2023-05-18T15:06:14.672811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200 25
 
1.7%
9600 24
 
1.6%
6000 17
 
1.2%
9000 14
 
1.0%
8400 14
 
1.0%
10800 14
 
1.0%
1680 10
 
0.7%
7500 9
 
0.6%
9100 8
 
0.5%
8125 8
 
0.5%
Other values (1063) 1317
90.2%
ValueCountFrequency (%)
1300 1
 
0.1%
1477 1
 
0.1%
1491 1
 
0.1%
1526 1
 
0.1%
1533 2
 
0.1%
1596 1
 
0.1%
1680 10
0.7%
1869 1
 
0.1%
1890 2
 
0.1%
1920 1
 
0.1%
ValueCountFrequency (%)
215245 1
0.1%
164660 1
0.1%
159000 1
0.1%
115149 1
0.1%
70761 1
0.1%
63887 1
0.1%
57200 1
0.1%
53504 1
0.1%
53227 1
0.1%
53107 1
0.1%

OverallQual
Real number (ℝ)

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0993151
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:14.719009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3829965
Coefficient of variation (CV)0.22674621
Kurtosis0.096292778
Mean6.0993151
Median Absolute Deviation (MAD)1
Skewness0.21694393
Sum8905
Variance1.9126794
MonotonicityNot monotonic
2023-05-18T15:06:14.756139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 397
27.2%
6 374
25.6%
7 319
21.8%
8 168
11.5%
4 116
 
7.9%
9 43
 
2.9%
3 20
 
1.4%
10 18
 
1.2%
2 3
 
0.2%
1 2
 
0.1%
ValueCountFrequency (%)
1 2
 
0.1%
2 3
 
0.2%
3 20
 
1.4%
4 116
 
7.9%
5 397
27.2%
6 374
25.6%
7 319
21.8%
8 168
11.5%
9 43
 
2.9%
10 18
 
1.2%
ValueCountFrequency (%)
10 18
 
1.2%
9 43
 
2.9%
8 168
11.5%
7 319
21.8%
6 374
25.6%
5 397
27.2%
4 116
 
7.9%
3 20
 
1.4%
2 3
 
0.2%
1 2
 
0.1%

OverallCond
Real number (ℝ)

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5753425
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:14.796622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1127993
Coefficient of variation (CV)0.199593
Kurtosis1.1064135
Mean5.5753425
Median Absolute Deviation (MAD)0
Skewness0.69306747
Sum8140
Variance1.2383224
MonotonicityNot monotonic
2023-05-18T15:06:14.837336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
4 57
 
3.9%
3 25
 
1.7%
9 22
 
1.5%
2 5
 
0.3%
1 1
 
0.1%
ValueCountFrequency (%)
1 1
 
0.1%
2 5
 
0.3%
3 25
 
1.7%
4 57
 
3.9%
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
9 22
 
1.5%
ValueCountFrequency (%)
9 22
 
1.5%
8 72
 
4.9%
7 205
 
14.0%
6 252
 
17.3%
5 821
56.2%
4 57
 
3.9%
3 25
 
1.7%
2 5
 
0.3%
1 1
 
0.1%

YearBuilt
Real number (ℝ)

Distinct112
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.2678
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:14.891097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1916
Q11954
median1973
Q32000
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.202904
Coefficient of variation (CV)0.015321563
Kurtosis-0.43955194
Mean1971.2678
Median Absolute Deviation (MAD)25
Skewness-0.61346117
Sum2878051
Variance912.21541
MonotonicityNot monotonic
2023-05-18T15:06:14.946658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2006 67
 
4.6%
2005 64
 
4.4%
2004 54
 
3.7%
2007 49
 
3.4%
2003 45
 
3.1%
1976 33
 
2.3%
1977 32
 
2.2%
1920 30
 
2.1%
1959 26
 
1.8%
1998 25
 
1.7%
Other values (102) 1035
70.9%
ValueCountFrequency (%)
1872 1
 
0.1%
1875 1
 
0.1%
1880 4
 
0.3%
1882 1
 
0.1%
1885 2
 
0.1%
1890 2
 
0.1%
1892 2
 
0.1%
1893 1
 
0.1%
1898 1
 
0.1%
1900 10
0.7%
ValueCountFrequency (%)
2010 1
 
0.1%
2009 18
 
1.2%
2008 23
 
1.6%
2007 49
3.4%
2006 67
4.6%
2005 64
4.4%
2004 54
3.7%
2003 45
3.1%
2002 23
 
1.6%
2001 20
 
1.4%

YearRemodAdd
Real number (ℝ)

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.8658
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:15.007075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11967
median1994
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.645407
Coefficient of variation (CV)0.010401412
Kurtosis-1.2722452
Mean1984.8658
Median Absolute Deviation (MAD)13
Skewness-0.503562
Sum2897904
Variance426.23282
MonotonicityNot monotonic
2023-05-18T15:06:15.064851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 178
 
12.2%
2006 97
 
6.6%
2007 76
 
5.2%
2005 73
 
5.0%
2004 62
 
4.2%
2000 55
 
3.8%
2003 51
 
3.5%
2002 48
 
3.3%
2008 40
 
2.7%
1996 36
 
2.5%
Other values (51) 744
51.0%
ValueCountFrequency (%)
1950 178
12.2%
1951 4
 
0.3%
1952 5
 
0.3%
1953 10
 
0.7%
1954 14
 
1.0%
1955 9
 
0.6%
1956 10
 
0.7%
1957 9
 
0.6%
1958 15
 
1.0%
1959 18
 
1.2%
ValueCountFrequency (%)
2010 6
 
0.4%
2009 23
 
1.6%
2008 40
2.7%
2007 76
5.2%
2006 97
6.6%
2005 73
5.0%
2004 62
4.2%
2003 51
3.5%
2002 48
3.3%
2001 21
 
1.4%

BsmtFinSF1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct637
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443.63973
Minimum0
Maximum5644
Zeros467
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:15.124240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median383.5
Q3712.25
95-th percentile1274
Maximum5644
Range5644
Interquartile range (IQR)712.25

Descriptive statistics

Standard deviation456.09809
Coefficient of variation (CV)1.0280822
Kurtosis11.118236
Mean443.63973
Median Absolute Deviation (MAD)383.5
Skewness1.6855031
Sum647714
Variance208025.47
MonotonicityNot monotonic
2023-05-18T15:06:15.177095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 467
32.0%
24 12
 
0.8%
16 9
 
0.6%
686 5
 
0.3%
662 5
 
0.3%
20 5
 
0.3%
936 5
 
0.3%
616 5
 
0.3%
560 4
 
0.3%
553 4
 
0.3%
Other values (627) 939
64.3%
ValueCountFrequency (%)
0 467
32.0%
2 1
 
0.1%
16 9
 
0.6%
20 5
 
0.3%
24 12
 
0.8%
25 1
 
0.1%
27 1
 
0.1%
28 3
 
0.2%
33 1
 
0.1%
35 1
 
0.1%
ValueCountFrequency (%)
5644 1
0.1%
2260 1
0.1%
2188 1
0.1%
2096 1
0.1%
1904 1
0.1%
1880 1
0.1%
1810 1
0.1%
1767 1
0.1%
1721 1
0.1%
1696 1
0.1%

BsmtFinSF2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct144
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.549315
Minimum0
Maximum1474
Zeros1293
Zeros (%)88.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:15.234614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile396.2
Maximum1474
Range1474
Interquartile range (IQR)0

Descriptive statistics

Standard deviation161.31927
Coefficient of variation (CV)3.4655563
Kurtosis20.113338
Mean46.549315
Median Absolute Deviation (MAD)0
Skewness4.2552611
Sum67962
Variance26023.908
MonotonicityNot monotonic
2023-05-18T15:06:15.290426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1293
88.6%
180 5
 
0.3%
374 3
 
0.2%
551 2
 
0.1%
147 2
 
0.1%
294 2
 
0.1%
391 2
 
0.1%
539 2
 
0.1%
96 2
 
0.1%
480 2
 
0.1%
Other values (134) 145
 
9.9%
ValueCountFrequency (%)
0 1293
88.6%
28 1
 
0.1%
32 1
 
0.1%
35 1
 
0.1%
40 1
 
0.1%
41 2
 
0.1%
64 2
 
0.1%
68 1
 
0.1%
80 1
 
0.1%
81 1
 
0.1%
ValueCountFrequency (%)
1474 1
0.1%
1127 1
0.1%
1120 1
0.1%
1085 1
0.1%
1080 1
0.1%
1063 1
0.1%
1061 1
0.1%
1057 1
0.1%
1031 1
0.1%
1029 1
0.1%

BsmtUnfSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct780
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.24041
Minimum0
Maximum2336
Zeros118
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:15.350213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1223
median477.5
Q3808
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)585

Descriptive statistics

Standard deviation441.86696
Coefficient of variation (CV)0.77897651
Kurtosis0.47499399
Mean567.24041
Median Absolute Deviation (MAD)288
Skewness0.92026845
Sum828171
Variance195246.41
MonotonicityNot monotonic
2023-05-18T15:06:15.406488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118
 
8.1%
728 9
 
0.6%
384 8
 
0.5%
600 7
 
0.5%
300 7
 
0.5%
572 7
 
0.5%
270 6
 
0.4%
625 6
 
0.4%
672 6
 
0.4%
440 6
 
0.4%
Other values (770) 1280
87.7%
ValueCountFrequency (%)
0 118
8.1%
14 1
 
0.1%
15 1
 
0.1%
23 2
 
0.1%
26 1
 
0.1%
29 1
 
0.1%
30 1
 
0.1%
32 2
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
ValueCountFrequency (%)
2336 1
0.1%
2153 1
0.1%
2121 1
0.1%
2046 1
0.1%
2042 1
0.1%
2002 1
0.1%
1969 1
0.1%
1935 1
0.1%
1926 1
0.1%
1907 1
0.1%

TotalBsmtSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct721
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1057.4295
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:15.464401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519.3
Q1795.75
median991.5
Q31298.25
95-th percentile1753
Maximum6110
Range6110
Interquartile range (IQR)502.5

Descriptive statistics

Standard deviation438.70532
Coefficient of variation (CV)0.41487905
Kurtosis13.250483
Mean1057.4295
Median Absolute Deviation (MAD)234.5
Skewness1.5242545
Sum1543847
Variance192462.36
MonotonicityNot monotonic
2023-05-18T15:06:15.519621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
2.5%
864 35
 
2.4%
672 17
 
1.2%
912 15
 
1.0%
1040 14
 
1.0%
816 13
 
0.9%
768 12
 
0.8%
728 12
 
0.8%
894 11
 
0.8%
780 11
 
0.8%
Other values (711) 1283
87.9%
ValueCountFrequency (%)
0 37
2.5%
105 1
 
0.1%
190 1
 
0.1%
264 3
 
0.2%
270 1
 
0.1%
290 1
 
0.1%
319 1
 
0.1%
360 1
 
0.1%
372 1
 
0.1%
384 7
 
0.5%
ValueCountFrequency (%)
6110 1
0.1%
3206 1
0.1%
3200 1
0.1%
3138 1
0.1%
3094 1
0.1%
2633 1
0.1%
2524 1
0.1%
2444 1
0.1%
2396 1
0.1%
2392 1
0.1%

1stFlrSF
Real number (ℝ)

Distinct753
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.6267
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:15.577903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.95
Q1882
median1087
Q31391.25
95-th percentile1831.25
Maximum4692
Range4358
Interquartile range (IQR)509.25

Descriptive statistics

Standard deviation386.58774
Coefficient of variation (CV)0.33251235
Kurtosis5.7458415
Mean1162.6267
Median Absolute Deviation (MAD)234.5
Skewness1.3767566
Sum1697435
Variance149450.08
MonotonicityNot monotonic
2023-05-18T15:06:15.632129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 25
 
1.7%
1040 16
 
1.1%
912 14
 
1.0%
894 12
 
0.8%
848 12
 
0.8%
672 11
 
0.8%
630 9
 
0.6%
816 9
 
0.6%
483 7
 
0.5%
960 7
 
0.5%
Other values (743) 1338
91.6%
ValueCountFrequency (%)
334 1
 
0.1%
372 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
483 7
0.5%
495 1
 
0.1%
520 5
0.3%
525 1
 
0.1%
526 1
 
0.1%
536 1
 
0.1%
ValueCountFrequency (%)
4692 1
0.1%
3228 1
0.1%
3138 1
0.1%
2898 1
0.1%
2633 1
0.1%
2524 1
0.1%
2515 1
0.1%
2444 1
0.1%
2411 1
0.1%
2402 1
0.1%

2ndFlrSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct417
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.99247
Minimum0
Maximum2065
Zeros829
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:15.691913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1141.05
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation436.52844
Coefficient of variation (CV)1.2580343
Kurtosis-0.55346356
Mean346.99247
Median Absolute Deviation (MAD)0
Skewness0.81302982
Sum506609
Variance190557.08
MonotonicityNot monotonic
2023-05-18T15:06:15.746740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 829
56.8%
728 10
 
0.7%
504 9
 
0.6%
546 8
 
0.5%
672 8
 
0.5%
600 7
 
0.5%
720 7
 
0.5%
896 6
 
0.4%
862 5
 
0.3%
780 5
 
0.3%
Other values (407) 566
38.8%
ValueCountFrequency (%)
0 829
56.8%
110 1
 
0.1%
167 1
 
0.1%
192 1
 
0.1%
208 1
 
0.1%
213 1
 
0.1%
220 1
 
0.1%
224 1
 
0.1%
240 2
 
0.1%
252 2
 
0.1%
ValueCountFrequency (%)
2065 1
0.1%
1872 1
0.1%
1818 1
0.1%
1796 1
0.1%
1611 1
0.1%
1589 1
0.1%
1540 1
0.1%
1538 1
0.1%
1523 1
0.1%
1519 1
0.1%

LowQualFinSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8445205
Minimum0
Maximum572
Zeros1434
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:15.794883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum572
Range572
Interquartile range (IQR)0

Descriptive statistics

Standard deviation48.623081
Coefficient of variation (CV)8.3194303
Kurtosis83.234817
Mean5.8445205
Median Absolute Deviation (MAD)0
Skewness9.0113413
Sum8533
Variance2364.204
MonotonicityNot monotonic
2023-05-18T15:06:15.840320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1434
98.2%
80 3
 
0.2%
360 2
 
0.1%
205 1
 
0.1%
479 1
 
0.1%
397 1
 
0.1%
514 1
 
0.1%
120 1
 
0.1%
481 1
 
0.1%
232 1
 
0.1%
Other values (14) 14
 
1.0%
ValueCountFrequency (%)
0 1434
98.2%
53 1
 
0.1%
80 3
 
0.2%
120 1
 
0.1%
144 1
 
0.1%
156 1
 
0.1%
205 1
 
0.1%
232 1
 
0.1%
234 1
 
0.1%
360 2
 
0.1%
ValueCountFrequency (%)
572 1
0.1%
528 1
0.1%
515 1
0.1%
514 1
0.1%
513 1
0.1%
481 1
0.1%
479 1
0.1%
473 1
0.1%
420 1
0.1%
397 1
0.1%

GrLivArea
Real number (ℝ)

Distinct861
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515.4637
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:15.894611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11129.5
median1464
Q31776.75
95-th percentile2466.1
Maximum5642
Range5308
Interquartile range (IQR)647.25

Descriptive statistics

Standard deviation525.48038
Coefficient of variation (CV)0.34674561
Kurtosis4.8951206
Mean1515.4637
Median Absolute Deviation (MAD)326
Skewness1.3665604
Sum2212577
Variance276129.63
MonotonicityNot monotonic
2023-05-18T15:06:15.952339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 22
 
1.5%
1040 14
 
1.0%
894 11
 
0.8%
1456 10
 
0.7%
848 10
 
0.7%
1200 9
 
0.6%
912 9
 
0.6%
816 8
 
0.5%
1092 8
 
0.5%
1728 7
 
0.5%
Other values (851) 1352
92.6%
ValueCountFrequency (%)
334 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
520 1
 
0.1%
605 1
 
0.1%
616 1
 
0.1%
630 6
0.4%
672 2
 
0.1%
691 1
 
0.1%
693 1
 
0.1%
ValueCountFrequency (%)
5642 1
0.1%
4676 1
0.1%
4476 1
0.1%
4316 1
0.1%
3627 1
0.1%
3608 1
0.1%
3493 1
0.1%
3447 1
0.1%
3395 1
0.1%
3279 1
0.1%

BsmtFullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
856 
1
588 
2
 
15
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Length

2023-05-18T15:06:16.002500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:16.057691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

BsmtHalfBath
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1378 
1
 
80
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Length

2023-05-18T15:06:16.100798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:16.147955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

FullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2
768 
1
650 
3
 
33
0
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Length

2023-05-18T15:06:16.189088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:16.238580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

HalfBath
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
913 
1
535 
2
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Length

2023-05-18T15:06:16.281414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:16.329154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

BedroomAbvGr
Real number (ℝ)

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8664384
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:16.366952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81577804
Coefficient of variation (CV)0.2845964
Kurtosis2.2308746
Mean2.8664384
Median Absolute Deviation (MAD)0
Skewness0.2117901
Sum4185
Variance0.66549382
MonotonicityNot monotonic
2023-05-18T15:06:16.406628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 804
55.1%
2 358
24.5%
4 213
 
14.6%
1 50
 
3.4%
5 21
 
1.4%
6 7
 
0.5%
0 6
 
0.4%
8 1
 
0.1%
ValueCountFrequency (%)
0 6
 
0.4%
1 50
 
3.4%
2 358
24.5%
3 804
55.1%
4 213
 
14.6%
5 21
 
1.4%
6 7
 
0.5%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
6 7
 
0.5%
5 21
 
1.4%
4 213
 
14.6%
3 804
55.1%
2 358
24.5%
1 50
 
3.4%
0 6
 
0.4%

KitchenAbvGr
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1392 
2
 
65
3
 
2
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Length

2023-05-18T15:06:16.454528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:16.502644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

TotRmsAbvGrd
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5178082
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:16.539337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile10
Maximum14
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6253933
Coefficient of variation (CV)0.24937728
Kurtosis0.88076157
Mean6.5178082
Median Absolute Deviation (MAD)1
Skewness0.67634084
Sum9516
Variance2.6419033
MonotonicityNot monotonic
2023-05-18T15:06:16.580779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 402
27.5%
7 329
22.5%
5 275
18.8%
8 187
12.8%
4 97
 
6.6%
9 75
 
5.1%
10 47
 
3.2%
11 18
 
1.2%
3 17
 
1.2%
12 11
 
0.8%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
2 1
 
0.1%
3 17
 
1.2%
4 97
 
6.6%
5 275
18.8%
6 402
27.5%
7 329
22.5%
8 187
12.8%
9 75
 
5.1%
10 47
 
3.2%
11 18
 
1.2%
ValueCountFrequency (%)
14 1
 
0.1%
12 11
 
0.8%
11 18
 
1.2%
10 47
 
3.2%
9 75
 
5.1%
8 187
12.8%
7 329
22.5%
6 402
27.5%
5 275
18.8%
4 97
 
6.6%

Fireplaces
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
690 
1
650 
2
115 
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Length

2023-05-18T15:06:16.625779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:16.674367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

GarageYrBlt
Real number (ℝ)

Distinct98
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1978.5062
Minimum1900
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:16.726726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1930
Q11962
median1978.5062
Q32001
95-th percentile2007
Maximum2010
Range110
Interquartile range (IQR)39

Descriptive statistics

Standard deviation23.994583
Coefficient of variation (CV)0.012127626
Kurtosis-0.2665029
Mean1978.5062
Median Absolute Deviation (MAD)20
Skewness-0.66817482
Sum2888619
Variance575.74003
MonotonicityNot monotonic
2023-05-18T15:06:16.785407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1978.506164 81
 
5.5%
2005 65
 
4.5%
2006 59
 
4.0%
2004 53
 
3.6%
2003 50
 
3.4%
2007 49
 
3.4%
1977 35
 
2.4%
1998 31
 
2.1%
1999 30
 
2.1%
1976 29
 
2.0%
Other values (88) 978
67.0%
ValueCountFrequency (%)
1900 1
 
0.1%
1906 1
 
0.1%
1908 1
 
0.1%
1910 3
 
0.2%
1914 2
 
0.1%
1915 2
 
0.1%
1916 5
 
0.3%
1918 2
 
0.1%
1920 14
1.0%
1921 3
 
0.2%
ValueCountFrequency (%)
2010 3
 
0.2%
2009 21
 
1.4%
2008 29
2.0%
2007 49
3.4%
2006 59
4.0%
2005 65
4.5%
2004 53
3.6%
2003 50
3.4%
2002 26
 
1.8%
2001 20
 
1.4%

GarageCars
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2
824 
1
369 
3
181 
0
 
81
4
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Length

2023-05-18T15:06:16.839717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:16.890649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

GarageArea
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct441
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.98014
Minimum0
Maximum1418
Zeros81
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:16.941743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1334.5
median480
Q3576
95-th percentile850.1
Maximum1418
Range1418
Interquartile range (IQR)241.5

Descriptive statistics

Standard deviation213.80484
Coefficient of variation (CV)0.45203768
Kurtosis0.9170672
Mean472.98014
Median Absolute Deviation (MAD)120
Skewness0.17998091
Sum690551
Variance45712.51
MonotonicityNot monotonic
2023-05-18T15:06:16.995393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
5.5%
440 49
 
3.4%
576 47
 
3.2%
240 38
 
2.6%
484 34
 
2.3%
528 33
 
2.3%
288 27
 
1.8%
400 25
 
1.7%
264 24
 
1.6%
480 24
 
1.6%
Other values (431) 1078
73.8%
ValueCountFrequency (%)
0 81
5.5%
160 2
 
0.1%
164 1
 
0.1%
180 9
 
0.6%
186 1
 
0.1%
189 1
 
0.1%
192 1
 
0.1%
198 1
 
0.1%
200 4
 
0.3%
205 3
 
0.2%
ValueCountFrequency (%)
1418 1
0.1%
1390 1
0.1%
1356 1
0.1%
1248 1
0.1%
1220 1
0.1%
1166 1
0.1%
1134 1
0.1%
1069 1
0.1%
1053 1
0.1%
1052 2
0.1%

WoodDeckSF
Real number (ℝ)

Distinct274
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.244521
Minimum0
Maximum857
Zeros761
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:17.056862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile335
Maximum857
Range857
Interquartile range (IQR)168

Descriptive statistics

Standard deviation125.33879
Coefficient of variation (CV)1.3299319
Kurtosis2.9929509
Mean94.244521
Median Absolute Deviation (MAD)0
Skewness1.5413758
Sum137597
Variance15709.813
MonotonicityNot monotonic
2023-05-18T15:06:17.110448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 761
52.1%
192 38
 
2.6%
100 36
 
2.5%
144 33
 
2.3%
120 31
 
2.1%
168 28
 
1.9%
140 15
 
1.0%
224 14
 
1.0%
208 10
 
0.7%
240 10
 
0.7%
Other values (264) 484
33.2%
ValueCountFrequency (%)
0 761
52.1%
12 2
 
0.1%
24 2
 
0.1%
26 2
 
0.1%
28 2
 
0.1%
30 1
 
0.1%
32 1
 
0.1%
33 1
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
ValueCountFrequency (%)
857 1
0.1%
736 1
0.1%
728 1
0.1%
670 1
0.1%
668 1
0.1%
635 1
0.1%
586 1
0.1%
576 1
0.1%
574 1
0.1%
550 1
0.1%

OpenPorchSF
Real number (ℝ)

Distinct202
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.660274
Minimum0
Maximum547
Zeros656
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:17.164578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q368
95-th percentile175.05
Maximum547
Range547
Interquartile range (IQR)68

Descriptive statistics

Standard deviation66.256028
Coefficient of variation (CV)1.4199665
Kurtosis8.4903358
Mean46.660274
Median Absolute Deviation (MAD)25
Skewness2.3643417
Sum68124
Variance4389.8612
MonotonicityNot monotonic
2023-05-18T15:06:17.217675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 656
44.9%
36 29
 
2.0%
48 22
 
1.5%
20 21
 
1.4%
40 19
 
1.3%
45 19
 
1.3%
24 16
 
1.1%
30 16
 
1.1%
60 15
 
1.0%
39 14
 
1.0%
Other values (192) 633
43.4%
ValueCountFrequency (%)
0 656
44.9%
4 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 3
 
0.2%
15 1
 
0.1%
16 8
 
0.5%
17 2
 
0.1%
18 5
 
0.3%
ValueCountFrequency (%)
547 1
0.1%
523 1
0.1%
502 1
0.1%
418 1
0.1%
406 1
0.1%
364 1
0.1%
341 1
0.1%
319 1
0.1%
312 2
0.1%
304 1
0.1%

EnclosedPorch
Real number (ℝ)

Distinct120
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.95411
Minimum0
Maximum552
Zeros1252
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:17.274343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile180.15
Maximum552
Range552
Interquartile range (IQR)0

Descriptive statistics

Standard deviation61.119149
Coefficient of variation (CV)2.7839502
Kurtosis10.430766
Mean21.95411
Median Absolute Deviation (MAD)0
Skewness3.0898719
Sum32053
Variance3735.5503
MonotonicityNot monotonic
2023-05-18T15:06:17.327955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1252
85.8%
112 15
 
1.0%
96 6
 
0.4%
192 5
 
0.3%
144 5
 
0.3%
120 5
 
0.3%
216 5
 
0.3%
156 4
 
0.3%
116 4
 
0.3%
252 4
 
0.3%
Other values (110) 155
 
10.6%
ValueCountFrequency (%)
0 1252
85.8%
19 1
 
0.1%
20 1
 
0.1%
24 1
 
0.1%
30 1
 
0.1%
32 2
 
0.1%
34 2
 
0.1%
36 2
 
0.1%
37 1
 
0.1%
39 2
 
0.1%
ValueCountFrequency (%)
552 1
0.1%
386 1
0.1%
330 1
0.1%
318 1
0.1%
301 1
0.1%
294 1
0.1%
293 1
0.1%
291 1
0.1%
286 1
0.1%
280 1
0.1%

3SsnPorch
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.409589
Minimum0
Maximum508
Zeros1436
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:17.376696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum508
Range508
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.317331
Coefficient of variation (CV)8.5984939
Kurtosis123.66238
Mean3.409589
Median Absolute Deviation (MAD)0
Skewness10.304342
Sum4978
Variance859.50587
MonotonicityNot monotonic
2023-05-18T15:06:17.422804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 1436
98.4%
168 3
 
0.2%
144 2
 
0.1%
180 2
 
0.1%
216 2
 
0.1%
290 1
 
0.1%
153 1
 
0.1%
96 1
 
0.1%
23 1
 
0.1%
162 1
 
0.1%
Other values (10) 10
 
0.7%
ValueCountFrequency (%)
0 1436
98.4%
23 1
 
0.1%
96 1
 
0.1%
130 1
 
0.1%
140 1
 
0.1%
144 2
 
0.1%
153 1
 
0.1%
162 1
 
0.1%
168 3
 
0.2%
180 2
 
0.1%
ValueCountFrequency (%)
508 1
0.1%
407 1
0.1%
320 1
0.1%
304 1
0.1%
290 1
0.1%
245 1
0.1%
238 1
0.1%
216 2
0.1%
196 1
0.1%
182 1
0.1%

ScreenPorch
Real number (ℝ)

Distinct76
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.060959
Minimum0
Maximum480
Zeros1344
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:17.475931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile160
Maximum480
Range480
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.757415
Coefficient of variation (CV)3.7021159
Kurtosis18.439068
Mean15.060959
Median Absolute Deviation (MAD)0
Skewness4.1222137
Sum21989
Variance3108.8894
MonotonicityNot monotonic
2023-05-18T15:06:17.536836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1344
92.1%
192 6
 
0.4%
120 5
 
0.3%
224 5
 
0.3%
189 4
 
0.3%
180 4
 
0.3%
147 3
 
0.2%
90 3
 
0.2%
160 3
 
0.2%
144 3
 
0.2%
Other values (66) 80
 
5.5%
ValueCountFrequency (%)
0 1344
92.1%
40 1
 
0.1%
53 1
 
0.1%
60 1
 
0.1%
63 1
 
0.1%
80 1
 
0.1%
90 3
 
0.2%
95 1
 
0.1%
99 1
 
0.1%
100 2
 
0.1%
ValueCountFrequency (%)
480 1
0.1%
440 1
0.1%
410 1
0.1%
396 1
0.1%
385 1
0.1%
374 1
0.1%
322 1
0.1%
312 1
0.1%
291 1
0.1%
288 2
0.1%

PoolArea
Real number (ℝ)

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7589041
Minimum0
Maximum738
Zeros1453
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:17.583678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum738
Range738
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.177307
Coefficient of variation (CV)14.562778
Kurtosis223.2685
Mean2.7589041
Median Absolute Deviation (MAD)0
Skewness14.828374
Sum4028
Variance1614.216
MonotonicityNot monotonic
2023-05-18T15:06:17.622775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1453
99.5%
512 1
 
0.1%
648 1
 
0.1%
576 1
 
0.1%
555 1
 
0.1%
480 1
 
0.1%
519 1
 
0.1%
738 1
 
0.1%
ValueCountFrequency (%)
0 1453
99.5%
480 1
 
0.1%
512 1
 
0.1%
519 1
 
0.1%
555 1
 
0.1%
576 1
 
0.1%
648 1
 
0.1%
738 1
 
0.1%
ValueCountFrequency (%)
738 1
 
0.1%
648 1
 
0.1%
576 1
 
0.1%
555 1
 
0.1%
519 1
 
0.1%
512 1
 
0.1%
480 1
 
0.1%
0 1453
99.5%

MoSold
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3219178
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-05-18T15:06:17.669209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7036262
Coefficient of variation (CV)0.42765918
Kurtosis-0.40410934
Mean6.3219178
Median Absolute Deviation (MAD)2
Skewness0.21205299
Sum9230
Variance7.3095947
MonotonicityNot monotonic
2023-05-18T15:06:17.709522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 253
17.3%
7 234
16.0%
5 204
14.0%
4 141
9.7%
8 122
8.4%
3 106
7.3%
10 89
 
6.1%
11 79
 
5.4%
9 63
 
4.3%
12 59
 
4.0%
Other values (2) 110
7.5%
ValueCountFrequency (%)
1 58
 
4.0%
2 52
 
3.6%
3 106
7.3%
4 141
9.7%
5 204
14.0%
6 253
17.3%
7 234
16.0%
8 122
8.4%
9 63
 
4.3%
10 89
 
6.1%
ValueCountFrequency (%)
12 59
 
4.0%
11 79
 
5.4%
10 89
 
6.1%
9 63
 
4.3%
8 122
8.4%
7 234
16.0%
6 253
17.3%
5 204
14.0%
4 141
9.7%
3 106
7.3%

YrSold
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2009
338 
2007
329 
2006
314 
2008
304 
2010
175 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5840
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008
2nd row2007
3rd row2008
4th row2006
5th row2008

Common Values

ValueCountFrequency (%)
2009 338
23.2%
2007 329
22.5%
2006 314
21.5%
2008 304
20.8%
2010 175
12.0%

Length

2023-05-18T15:06:17.753922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:17.807080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2009 338
23.2%
2007 329
22.5%
2006 314
21.5%
2008 304
20.8%
2010 175
12.0%

Most occurring characters

ValueCountFrequency (%)
0 2920
50.0%
2 1460
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 304
 
5.2%
1 175
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5840
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2920
50.0%
2 1460
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 304
 
5.2%
1 175
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2920
50.0%
2 1460
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 304
 
5.2%
1 175
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2920
50.0%
2 1460
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 304
 
5.2%
1 175
 
3.0%

MSZoning_C (all)
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1450 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1450
99.3%
1 10
 
0.7%

Length

2023-05-18T15:06:17.855721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:17.901548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1450
99.3%
1 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1450
99.3%
1 10
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1450
99.3%
1 10
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1450
99.3%
1 10
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1450
99.3%
1 10
 
0.7%

MSZoning_FV
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1395 
1
 
65

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1395
95.5%
1 65
 
4.5%

Length

2023-05-18T15:06:17.939865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:17.986407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1395
95.5%
1 65
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 1395
95.5%
1 65
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1395
95.5%
1 65
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1395
95.5%
1 65
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1395
95.5%
1 65
 
4.5%

MSZoning_RH
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1444 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1444
98.9%
1 16
 
1.1%

Length

2023-05-18T15:06:18.025085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:18.072316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1444
98.9%
1 16
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 1444
98.9%
1 16
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1444
98.9%
1 16
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1444
98.9%
1 16
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1444
98.9%
1 16
 
1.1%

MSZoning_RL
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1151 
0
309 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1151
78.8%
0 309
 
21.2%

Length

2023-05-18T15:06:18.110291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:18.157961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1151
78.8%
0 309
 
21.2%

Most occurring characters

ValueCountFrequency (%)
1 1151
78.8%
0 309
 
21.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1151
78.8%
0 309
 
21.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1151
78.8%
0 309
 
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1151
78.8%
0 309
 
21.2%

MSZoning_RM
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1242 
1
218 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1242
85.1%
1 218
 
14.9%

Length

2023-05-18T15:06:18.198766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:18.245852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1242
85.1%
1 218
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 1242
85.1%
1 218
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1242
85.1%
1 218
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1242
85.1%
1 218
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1242
85.1%
1 218
 
14.9%

LandContour_Bnk
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1397 
1
 
63

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1397
95.7%
1 63
 
4.3%

Length

2023-05-18T15:06:18.284728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:18.331299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1397
95.7%
1 63
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 1397
95.7%
1 63
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1397
95.7%
1 63
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1397
95.7%
1 63
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1397
95.7%
1 63
 
4.3%

LandContour_HLS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1410 
1
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1410
96.6%
1 50
 
3.4%

Length

2023-05-18T15:06:18.370110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:18.416406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1410
96.6%
1 50
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 1410
96.6%
1 50
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1410
96.6%
1 50
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1410
96.6%
1 50
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1410
96.6%
1 50
 
3.4%

LandContour_Low
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1424 
1
 
36

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1424
97.5%
1 36
 
2.5%

Length

2023-05-18T15:06:18.454589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:18.500872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1424
97.5%
1 36
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 1424
97.5%
1 36
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1424
97.5%
1 36
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1424
97.5%
1 36
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1424
97.5%
1 36
 
2.5%

LandContour_Lvl
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1311 
0
149 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1311
89.8%
0 149
 
10.2%

Length

2023-05-18T15:06:18.539258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:18.586126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1311
89.8%
0 149
 
10.2%

Most occurring characters

ValueCountFrequency (%)
1 1311
89.8%
0 149
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1311
89.8%
0 149
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1311
89.8%
0 149
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1311
89.8%
0 149
 
10.2%

LotConfig_Corner
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1197 
1
263 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1197
82.0%
1 263
 
18.0%

Length

2023-05-18T15:06:18.624838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:18.672127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1197
82.0%
1 263
 
18.0%

Most occurring characters

ValueCountFrequency (%)
0 1197
82.0%
1 263
 
18.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1197
82.0%
1 263
 
18.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1197
82.0%
1 263
 
18.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1197
82.0%
1 263
 
18.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1366 
1
 
94

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1366
93.6%
1 94
 
6.4%

Length

2023-05-18T15:06:18.711094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:18.757722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1366
93.6%
1 94
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 1366
93.6%
1 94
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1366
93.6%
1 94
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1366
93.6%
1 94
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1366
93.6%
1 94
 
6.4%

LotConfig_FR2
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1413 
1
 
47

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1413
96.8%
1 47
 
3.2%

Length

2023-05-18T15:06:18.796217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:18.844960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1413
96.8%
1 47
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 1413
96.8%
1 47
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1413
96.8%
1 47
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1413
96.8%
1 47
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1413
96.8%
1 47
 
3.2%

LotConfig_FR3
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1456 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Length

2023-05-18T15:06:18.883678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:18.931387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

LotConfig_Inside
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1052 
0
408 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 1052
72.1%
0 408
 
27.9%

Length

2023-05-18T15:06:18.969853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:19.016624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1052
72.1%
0 408
 
27.9%

Most occurring characters

ValueCountFrequency (%)
1 1052
72.1%
0 408
 
27.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1052
72.1%
0 408
 
27.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1052
72.1%
0 408
 
27.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1052
72.1%
0 408
 
27.9%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1443 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1443
98.8%
1 17
 
1.2%

Length

2023-05-18T15:06:19.058219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:19.105045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1443
98.8%
1 17
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 1443
98.8%
1 17
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1443
98.8%
1 17
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1443
98.8%
1 17
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1443
98.8%
1 17
 
1.2%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1458 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Length

2023-05-18T15:06:19.143387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:19.189238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1444 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1444
98.9%
1 16
 
1.1%

Length

2023-05-18T15:06:19.227730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:19.277500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1444
98.9%
1 16
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 1444
98.9%
1 16
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1444
98.9%
1 16
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1444
98.9%
1 16
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1444
98.9%
1 16
 
1.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1402 
1
 
58

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1402
96.0%
1 58
 
4.0%

Length

2023-05-18T15:06:19.316712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:19.362681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1402
96.0%
1 58
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 1402
96.0%
1 58
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1402
96.0%
1 58
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1402
96.0%
1 58
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1402
96.0%
1 58
 
4.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1432 
1
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1432
98.1%
1 28
 
1.9%

Length

2023-05-18T15:06:19.401494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:19.446957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1432
98.1%
1 28
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 1432
98.1%
1 28
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1432
98.1%
1 28
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1432
98.1%
1 28
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1432
98.1%
1 28
 
1.9%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1310 
1
150 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1310
89.7%
1 150
 
10.3%

Length

2023-05-18T15:06:19.486942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:19.533462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1310
89.7%
1 150
 
10.3%

Most occurring characters

ValueCountFrequency (%)
0 1310
89.7%
1 150
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1310
89.7%
1 150
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1310
89.7%
1 150
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1310
89.7%
1 150
 
10.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1409 
1
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1409
96.5%
1 51
 
3.5%

Length

2023-05-18T15:06:19.572865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:19.618448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1409
96.5%
1 51
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 1409
96.5%
1 51
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1409
96.5%
1 51
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1409
96.5%
1 51
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1409
96.5%
1 51
 
3.5%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1360 
1
 
100

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1360
93.2%
1 100
 
6.8%

Length

2023-05-18T15:06:19.656464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:19.702403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1360
93.2%
1 100
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 1360
93.2%
1 100
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1360
93.2%
1 100
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1360
93.2%
1 100
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1360
93.2%
1 100
 
6.8%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1381 
1
 
79

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1381
94.6%
1 79
 
5.4%

Length

2023-05-18T15:06:19.741113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:19.787034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1381
94.6%
1 79
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 1381
94.6%
1 79
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1381
94.6%
1 79
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1381
94.6%
1 79
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1381
94.6%
1 79
 
5.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1423 
1
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1423
97.5%
1 37
 
2.5%

Length

2023-05-18T15:06:19.825952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:19.871794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1423
97.5%
1 37
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 1423
97.5%
1 37
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1423
97.5%
1 37
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1423
97.5%
1 37
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1423
97.5%
1 37
 
2.5%

Neighborhood_MeadowV
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1443 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1443
98.8%
1 17
 
1.2%

Length

2023-05-18T15:06:19.911901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:19.959989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1443
98.8%
1 17
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 1443
98.8%
1 17
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1443
98.8%
1 17
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1443
98.8%
1 17
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1443
98.8%
1 17
 
1.2%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1411 
1
 
49

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1411
96.6%
1 49
 
3.4%

Length

2023-05-18T15:06:19.999527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:20.049091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1411
96.6%
1 49
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 1411
96.6%
1 49
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1411
96.6%
1 49
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1411
96.6%
1 49
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1411
96.6%
1 49
 
3.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1235 
1
225 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1235
84.6%
1 225
 
15.4%

Length

2023-05-18T15:06:20.087638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:20.134466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1235
84.6%
1 225
 
15.4%

Most occurring characters

ValueCountFrequency (%)
0 1235
84.6%
1 225
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1235
84.6%
1 225
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1235
84.6%
1 225
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1235
84.6%
1 225
 
15.4%

Neighborhood_NPkVill
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1451 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Length

2023-05-18T15:06:20.173269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:20.219697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1387 
1
 
73

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1387
95.0%
1 73
 
5.0%

Length

2023-05-18T15:06:20.257435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:20.303849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1387
95.0%
1 73
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 1387
95.0%
1 73
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1387
95.0%
1 73
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1387
95.0%
1 73
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1387
95.0%
1 73
 
5.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1419 
1
 
41

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1419
97.2%
1 41
 
2.8%

Length

2023-05-18T15:06:20.341633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:20.389042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1419
97.2%
1 41
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 1419
97.2%
1 41
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1419
97.2%
1 41
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1419
97.2%
1 41
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1419
97.2%
1 41
 
2.8%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1383 
1
 
77

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1383
94.7%
1 77
 
5.3%

Length

2023-05-18T15:06:20.427737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:20.474042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1383
94.7%
1 77
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 1383
94.7%
1 77
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1383
94.7%
1 77
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1383
94.7%
1 77
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1383
94.7%
1 77
 
5.3%

Neighborhood_OldTown
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1347 
1
 
113

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1347
92.3%
1 113
 
7.7%

Length

2023-05-18T15:06:20.512327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:20.987429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1347
92.3%
1 113
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 1347
92.3%
1 113
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1347
92.3%
1 113
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1347
92.3%
1 113
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1347
92.3%
1 113
 
7.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1435 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Length

2023-05-18T15:06:21.027086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:21.075426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1386 
1
 
74

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1386
94.9%
1 74
 
5.1%

Length

2023-05-18T15:06:21.114087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:21.160435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1386
94.9%
1 74
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 1386
94.9%
1 74
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1386
94.9%
1 74
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1386
94.9%
1 74
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1386
94.9%
1 74
 
5.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1401 
1
 
59

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1401
96.0%
1 59
 
4.0%

Length

2023-05-18T15:06:21.199828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:21.247029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1401
96.0%
1 59
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 1401
96.0%
1 59
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1401
96.0%
1 59
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1401
96.0%
1 59
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1401
96.0%
1 59
 
4.0%

Neighborhood_Somerst
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1374 
1
 
86

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1374
94.1%
1 86
 
5.9%

Length

2023-05-18T15:06:21.285034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:21.330995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1374
94.1%
1 86
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 1374
94.1%
1 86
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1374
94.1%
1 86
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1374
94.1%
1 86
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1374
94.1%
1 86
 
5.9%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1435 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Length

2023-05-18T15:06:21.369523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:21.416345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1422 
1
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1422
97.4%
1 38
 
2.6%

Length

2023-05-18T15:06:21.454175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:21.500609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1422
97.4%
1 38
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 1422
97.4%
1 38
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1422
97.4%
1 38
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1422
97.4%
1 38
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1422
97.4%
1 38
 
2.6%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1449 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1449
99.2%
1 11
 
0.8%

Length

2023-05-18T15:06:21.538139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:21.585559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1449
99.2%
1 11
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 1449
99.2%
1 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1449
99.2%
1 11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1449
99.2%
1 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1449
99.2%
1 11
 
0.8%

BldgType_1Fam
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1220 
0
240 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1220
83.6%
0 240
 
16.4%

Length

2023-05-18T15:06:21.624616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:21.672524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1220
83.6%
0 240
 
16.4%

Most occurring characters

ValueCountFrequency (%)
1 1220
83.6%
0 240
 
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1220
83.6%
0 240
 
16.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1220
83.6%
0 240
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1220
83.6%
0 240
 
16.4%

BldgType_2fmCon
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1429 
1
 
31

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1429
97.9%
1 31
 
2.1%

Length

2023-05-18T15:06:21.712788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:21.759165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1429
97.9%
1 31
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1429
97.9%
1 31
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1429
97.9%
1 31
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1429
97.9%
1 31
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1429
97.9%
1 31
 
2.1%

BldgType_Duplex
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1408 
1
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1408
96.4%
1 52
 
3.6%

Length

2023-05-18T15:06:21.797978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:21.844229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1408
96.4%
1 52
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 1408
96.4%
1 52
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1408
96.4%
1 52
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1408
96.4%
1 52
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1408
96.4%
1 52
 
3.6%

BldgType_Twnhs
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1417 
1
 
43

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1417
97.1%
1 43
 
2.9%

Length

2023-05-18T15:06:21.882525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:21.928886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1417
97.1%
1 43
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 1417
97.1%
1 43
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1417
97.1%
1 43
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1417
97.1%
1 43
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1417
97.1%
1 43
 
2.9%

BldgType_TwnhsE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1346 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1346
92.2%
1 114
 
7.8%

Length

2023-05-18T15:06:21.966670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:22.013748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1346
92.2%
1 114
 
7.8%

Most occurring characters

ValueCountFrequency (%)
0 1346
92.2%
1 114
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1346
92.2%
1 114
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1346
92.2%
1 114
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1346
92.2%
1 114
 
7.8%

HouseStyle_1.5Fin
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1306 
1
154 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1306
89.5%
1 154
 
10.5%

Length

2023-05-18T15:06:22.052147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:22.100850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1306
89.5%
1 154
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 1306
89.5%
1 154
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1306
89.5%
1 154
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1306
89.5%
1 154
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1306
89.5%
1 154
 
10.5%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1446 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Length

2023-05-18T15:06:22.140934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:22.187238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
734 
1
726 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 734
50.3%
1 726
49.7%

Length

2023-05-18T15:06:22.225607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:22.273431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 734
50.3%
1 726
49.7%

Most occurring characters

ValueCountFrequency (%)
0 734
50.3%
1 726
49.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 734
50.3%
1 726
49.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 734
50.3%
1 726
49.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 734
50.3%
1 726
49.7%

HouseStyle_2.5Fin
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1452 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1452
99.5%
1 8
 
0.5%

Length

2023-05-18T15:06:22.312486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:22.358903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1452
99.5%
1 8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1452
99.5%
1 8
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1452
99.5%
1 8
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1452
99.5%
1 8
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1452
99.5%
1 8
 
0.5%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1449 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1449
99.2%
1 11
 
0.8%

Length

2023-05-18T15:06:22.396867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:22.443201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1449
99.2%
1 11
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 1449
99.2%
1 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1449
99.2%
1 11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1449
99.2%
1 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1449
99.2%
1 11
 
0.8%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1015 
1
445 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1015
69.5%
1 445
30.5%

Length

2023-05-18T15:06:22.483285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:22.532732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1015
69.5%
1 445
30.5%

Most occurring characters

ValueCountFrequency (%)
0 1015
69.5%
1 445
30.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1015
69.5%
1 445
30.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1015
69.5%
1 445
30.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1015
69.5%
1 445
30.5%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1423 
1
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1423
97.5%
1 37
 
2.5%

Length

2023-05-18T15:06:22.573032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:22.618884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1423
97.5%
1 37
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 1423
97.5%
1 37
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1423
97.5%
1 37
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1423
97.5%
1 37
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1423
97.5%
1 37
 
2.5%

HouseStyle_SLvl
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1395 
1
 
65

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1395
95.5%
1 65
 
4.5%

Length

2023-05-18T15:06:22.657577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:22.703388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1395
95.5%
1 65
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 1395
95.5%
1 65
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1395
95.5%
1 65
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1395
95.5%
1 65
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1395
95.5%
1 65
 
4.5%

RoofStyle_Flat
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1447 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1447
99.1%
1 13
 
0.9%

Length

2023-05-18T15:06:22.742022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:22.787924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1447
99.1%
1 13
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 1447
99.1%
1 13
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1447
99.1%
1 13
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1447
99.1%
1 13
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1447
99.1%
1 13
 
0.9%

RoofStyle_Gable
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1141 
0
319 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1141
78.2%
0 319
 
21.8%

Length

2023-05-18T15:06:22.828120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:22.875367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1141
78.2%
0 319
 
21.8%

Most occurring characters

ValueCountFrequency (%)
1 1141
78.2%
0 319
 
21.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1141
78.2%
0 319
 
21.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1141
78.2%
0 319
 
21.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1141
78.2%
0 319
 
21.8%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1449 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1449
99.2%
1 11
 
0.8%

Length

2023-05-18T15:06:22.915146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:22.962456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1449
99.2%
1 11
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 1449
99.2%
1 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1449
99.2%
1 11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1449
99.2%
1 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1449
99.2%
1 11
 
0.8%

RoofStyle_Hip
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1174 
1
286 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1174
80.4%
1 286
 
19.6%

Length

2023-05-18T15:06:23.001490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:23.047853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1174
80.4%
1 286
 
19.6%

Most occurring characters

ValueCountFrequency (%)
0 1174
80.4%
1 286
 
19.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1174
80.4%
1 286
 
19.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1174
80.4%
1 286
 
19.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1174
80.4%
1 286
 
19.6%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1453 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Length

2023-05-18T15:06:23.087461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:23.135304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

RoofStyle_Shed
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1458 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Length

2023-05-18T15:06:23.174175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:23.221452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Exterior1st_AsbShng
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1440 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1440
98.6%
1 20
 
1.4%

Length

2023-05-18T15:06:23.260280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:23.306240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1440
98.6%
1 20
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 1440
98.6%
1 20
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1440
98.6%
1 20
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1440
98.6%
1 20
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1440
98.6%
1 20
 
1.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Length

2023-05-18T15:06:23.345787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:23.392339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1458 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Length

2023-05-18T15:06:23.431006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:23.476623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Exterior1st_BrkFace
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1410 
1
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1410
96.6%
1 50
 
3.4%

Length

2023-05-18T15:06:23.515975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:23.564829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1410
96.6%
1 50
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 1410
96.6%
1 50
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1410
96.6%
1 50
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1410
96.6%
1 50
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1410
96.6%
1 50
 
3.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Length

2023-05-18T15:06:23.604804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:23.651327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Exterior1st_CemntBd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1399 
1
 
61

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1399
95.8%
1 61
 
4.2%

Length

2023-05-18T15:06:23.689446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:23.736686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1399
95.8%
1 61
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 1399
95.8%
1 61
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1399
95.8%
1 61
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1399
95.8%
1 61
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1399
95.8%
1 61
 
4.2%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1238 
1
222 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1238
84.8%
1 222
 
15.2%

Length

2023-05-18T15:06:23.774348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:23.821513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1238
84.8%
1 222
 
15.2%

Most occurring characters

ValueCountFrequency (%)
0 1238
84.8%
1 222
 
15.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1238
84.8%
1 222
 
15.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1238
84.8%
1 222
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1238
84.8%
1 222
 
15.2%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Length

2023-05-18T15:06:23.860674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:23.908028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1240 
1
220 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1240
84.9%
1 220
 
15.1%

Length

2023-05-18T15:06:23.946523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:23.993578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1240
84.9%
1 220
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 1240
84.9%
1 220
 
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1240
84.9%
1 220
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1240
84.9%
1 220
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1240
84.9%
1 220
 
15.1%

Exterior1st_Plywood
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1352 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1352
92.6%
1 108
 
7.4%

Length

2023-05-18T15:06:24.032380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:24.080039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1352
92.6%
1 108
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 1352
92.6%
1 108
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1352
92.6%
1 108
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1352
92.6%
1 108
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1352
92.6%
1 108
 
7.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1458 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Length

2023-05-18T15:06:24.118177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:24.166228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Exterior1st_Stucco
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1435 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Length

2023-05-18T15:06:24.206016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:24.252752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
945 
1
515 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 945
64.7%
1 515
35.3%

Length

2023-05-18T15:06:24.291072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:24.338017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 945
64.7%
1 515
35.3%

Most occurring characters

ValueCountFrequency (%)
0 945
64.7%
1 515
35.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 945
64.7%
1 515
35.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 945
64.7%
1 515
35.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 945
64.7%
1 515
35.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1254 
1
206 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1254
85.9%
1 206
 
14.1%

Length

2023-05-18T15:06:24.379138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:24.426569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1254
85.9%
1 206
 
14.1%

Most occurring characters

ValueCountFrequency (%)
0 1254
85.9%
1 206
 
14.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1254
85.9%
1 206
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1254
85.9%
1 206
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1254
85.9%
1 206
 
14.1%

Exterior1st_WdShing
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1434 
1
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1434
98.2%
1 26
 
1.8%

Length

2023-05-18T15:06:24.465414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:24.511885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1434
98.2%
1 26
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 1434
98.2%
1 26
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1434
98.2%
1 26
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1434
98.2%
1 26
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1434
98.2%
1 26
 
1.8%

Exterior2nd_AsbShng
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1440 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1440
98.6%
1 20
 
1.4%

Length

2023-05-18T15:06:24.549757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:24.595367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1440
98.6%
1 20
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 1440
98.6%
1 20
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1440
98.6%
1 20
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1440
98.6%
1 20
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1440
98.6%
1 20
 
1.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1457 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Length

2023-05-18T15:06:24.634520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:24.679955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Exterior2nd_Brk Cmn
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1453 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Length

2023-05-18T15:06:24.718515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:24.764016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Exterior2nd_BrkFace
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1435 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Length

2023-05-18T15:06:24.803585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:24.850829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1435
98.3%
1 25
 
1.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Length

2023-05-18T15:06:24.889373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:24.935384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Exterior2nd_CmentBd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1400 
1
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1400
95.9%
1 60
 
4.1%

Length

2023-05-18T15:06:24.973659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:25.021507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1400
95.9%
1 60
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 1400
95.9%
1 60
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1400
95.9%
1 60
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1400
95.9%
1 60
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1400
95.9%
1 60
 
4.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1253 
1
207 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1253
85.8%
1 207
 
14.2%

Length

2023-05-18T15:06:25.060121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:25.106455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1253
85.8%
1 207
 
14.2%

Most occurring characters

ValueCountFrequency (%)
0 1253
85.8%
1 207
 
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1253
85.8%
1 207
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1253
85.8%
1 207
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1253
85.8%
1 207
 
14.2%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1450 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1450
99.3%
1 10
 
0.7%

Length

2023-05-18T15:06:25.146011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:25.192240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1450
99.3%
1 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1450
99.3%
1 10
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1450
99.3%
1 10
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1450
99.3%
1 10
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1450
99.3%
1 10
 
0.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1246 
1
214 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1246
85.3%
1 214
 
14.7%

Length

2023-05-18T15:06:25.232338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:25.279574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1246
85.3%
1 214
 
14.7%

Most occurring characters

ValueCountFrequency (%)
0 1246
85.3%
1 214
 
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1246
85.3%
1 214
 
14.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1246
85.3%
1 214
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1246
85.3%
1 214
 
14.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Length

2023-05-18T15:06:25.319562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:25.365424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Exterior2nd_Plywood
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1318 
1
142 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1318
90.3%
1 142
 
9.7%

Length

2023-05-18T15:06:25.404180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:25.450449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1318
90.3%
1 142
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 1318
90.3%
1 142
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1318
90.3%
1 142
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1318
90.3%
1 142
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1318
90.3%
1 142
 
9.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1455 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Length

2023-05-18T15:06:25.490653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:25.538237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Exterior2nd_Stucco
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1434 
1
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1434
98.2%
1 26
 
1.8%

Length

2023-05-18T15:06:25.576389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:25.622238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1434
98.2%
1 26
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 1434
98.2%
1 26
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1434
98.2%
1 26
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1434
98.2%
1 26
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1434
98.2%
1 26
 
1.8%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
956 
1
504 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 956
65.5%
1 504
34.5%

Length

2023-05-18T15:06:25.659970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:25.707663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 956
65.5%
1 504
34.5%

Most occurring characters

ValueCountFrequency (%)
0 956
65.5%
1 504
34.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 956
65.5%
1 504
34.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 956
65.5%
1 504
34.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 956
65.5%
1 504
34.5%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1263 
1
197 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1263
86.5%
1 197
 
13.5%

Length

2023-05-18T15:06:25.746618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:25.793198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1263
86.5%
1 197
 
13.5%

Most occurring characters

ValueCountFrequency (%)
0 1263
86.5%
1 197
 
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1263
86.5%
1 197
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1263
86.5%
1 197
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1263
86.5%
1 197
 
13.5%

Exterior2nd_Wd Shng
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1422 
1
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1422
97.4%
1 38
 
2.6%

Length

2023-05-18T15:06:25.832457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:25.878533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1422
97.4%
1 38
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 1422
97.4%
1 38
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1422
97.4%
1 38
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1422
97.4%
1 38
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1422
97.4%
1 38
 
2.6%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1445 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1445
99.0%
1 15
 
1.0%

Length

2023-05-18T15:06:25.916638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:25.968206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1445
99.0%
1 15
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 1445
99.0%
1 15
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1445
99.0%
1 15
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1445
99.0%
1 15
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1445
99.0%
1 15
 
1.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1015 
1
445 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1015
69.5%
1 445
30.5%

Length

2023-05-18T15:06:26.007017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:26.053931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1015
69.5%
1 445
30.5%

Most occurring characters

ValueCountFrequency (%)
0 1015
69.5%
1 445
30.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1015
69.5%
1 445
30.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1015
69.5%
1 445
30.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1015
69.5%
1 445
30.5%

MasVnrType_None
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
864 
0
596 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 864
59.2%
0 596
40.8%

Length

2023-05-18T15:06:26.092523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:26.139497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 864
59.2%
0 596
40.8%

Most occurring characters

ValueCountFrequency (%)
1 864
59.2%
0 596
40.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 864
59.2%
0 596
40.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 864
59.2%
0 596
40.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 864
59.2%
0 596
40.8%

MasVnrType_Stone
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1332 
1
 
128

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1332
91.2%
1 128
 
8.8%

Length

2023-05-18T15:06:26.178488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:26.224956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1332
91.2%
1 128
 
8.8%

Most occurring characters

ValueCountFrequency (%)
0 1332
91.2%
1 128
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1332
91.2%
1 128
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1332
91.2%
1 128
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1332
91.2%
1 128
 
8.8%

ExterQual_Ex
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1408 
1
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1408
96.4%
1 52
 
3.6%

Length

2023-05-18T15:06:26.264042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:26.311479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1408
96.4%
1 52
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 1408
96.4%
1 52
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1408
96.4%
1 52
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1408
96.4%
1 52
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1408
96.4%
1 52
 
3.6%

ExterQual_Fa
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1446 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Length

2023-05-18T15:06:26.350039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:26.396080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

ExterQual_Gd
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
972 
1
488 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 972
66.6%
1 488
33.4%

Length

2023-05-18T15:06:26.434156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:26.480673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 972
66.6%
1 488
33.4%

Most occurring characters

ValueCountFrequency (%)
0 972
66.6%
1 488
33.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 972
66.6%
1 488
33.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 972
66.6%
1 488
33.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 972
66.6%
1 488
33.4%

ExterQual_TA
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
906 
0
554 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 906
62.1%
0 554
37.9%

Length

2023-05-18T15:06:26.520760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:26.568664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 906
62.1%
0 554
37.9%

Most occurring characters

ValueCountFrequency (%)
1 906
62.1%
0 554
37.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 906
62.1%
0 554
37.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 906
62.1%
0 554
37.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 906
62.1%
0 554
37.9%

ExterCond_Ex
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1457 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Length

2023-05-18T15:06:26.608295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:26.655599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

ExterCond_Fa
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1432 
1
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1432
98.1%
1 28
 
1.9%

Length

2023-05-18T15:06:26.693883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:26.740396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1432
98.1%
1 28
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 1432
98.1%
1 28
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1432
98.1%
1 28
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1432
98.1%
1 28
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1432
98.1%
1 28
 
1.9%

ExterCond_Gd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1314 
1
146 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1314
90.0%
1 146
 
10.0%

Length

2023-05-18T15:06:26.778805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:26.824943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1314
90.0%
1 146
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 1314
90.0%
1 146
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1314
90.0%
1 146
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1314
90.0%
1 146
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1314
90.0%
1 146
 
10.0%

ExterCond_Po
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Length

2023-05-18T15:06:26.864694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:26.911872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

ExterCond_TA
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1282 
0
178 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1282
87.8%
0 178
 
12.2%

Length

2023-05-18T15:06:26.950222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:26.998198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1282
87.8%
0 178
 
12.2%

Most occurring characters

ValueCountFrequency (%)
1 1282
87.8%
0 178
 
12.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1282
87.8%
0 178
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1282
87.8%
0 178
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1282
87.8%
0 178
 
12.2%

Foundation_BrkTil
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1314 
1
146 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1314
90.0%
1 146
 
10.0%

Length

2023-05-18T15:06:27.039592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:27.086643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1314
90.0%
1 146
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 1314
90.0%
1 146
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1314
90.0%
1 146
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1314
90.0%
1 146
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1314
90.0%
1 146
 
10.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
826 
1
634 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 826
56.6%
1 634
43.4%

Length

2023-05-18T15:06:27.126332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:27.172409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 826
56.6%
1 634
43.4%

Most occurring characters

ValueCountFrequency (%)
0 826
56.6%
1 634
43.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826
56.6%
1 634
43.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826
56.6%
1 634
43.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826
56.6%
1 634
43.4%

Foundation_PConc
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
813 
1
647 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 813
55.7%
1 647
44.3%

Length

2023-05-18T15:06:27.212007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:27.258275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 813
55.7%
1 647
44.3%

Most occurring characters

ValueCountFrequency (%)
0 813
55.7%
1 647
44.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 813
55.7%
1 647
44.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 813
55.7%
1 647
44.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 813
55.7%
1 647
44.3%

Foundation_Slab
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1436 
1
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1436
98.4%
1 24
 
1.6%

Length

2023-05-18T15:06:27.297442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:27.343299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1436
98.4%
1 24
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 1436
98.4%
1 24
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1436
98.4%
1 24
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1436
98.4%
1 24
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1436
98.4%
1 24
 
1.6%

Foundation_Stone
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1454 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1454
99.6%
1 6
 
0.4%

Length

2023-05-18T15:06:27.381584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:27.428701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1454
99.6%
1 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1454
99.6%
1 6
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1454
99.6%
1 6
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1454
99.6%
1 6
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1454
99.6%
1 6
 
0.4%

Foundation_Wood
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1457 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Length

2023-05-18T15:06:27.468854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:27.515998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

BsmtQual_Ex
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1339 
1
 
121

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1339
91.7%
1 121
 
8.3%

Length

2023-05-18T15:06:27.556233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:27.604527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1339
91.7%
1 121
 
8.3%

Most occurring characters

ValueCountFrequency (%)
0 1339
91.7%
1 121
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1339
91.7%
1 121
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1339
91.7%
1 121
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1339
91.7%
1 121
 
8.3%

BsmtQual_Fa
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1425 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1425
97.6%
1 35
 
2.4%

Length

2023-05-18T15:06:27.645489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:27.691784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1425
97.6%
1 35
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 1425
97.6%
1 35
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1425
97.6%
1 35
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1425
97.6%
1 35
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1425
97.6%
1 35
 
2.4%

BsmtQual_Gd
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
842 
1
618 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 842
57.7%
1 618
42.3%

Length

2023-05-18T15:06:27.729759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:27.776648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 842
57.7%
1 618
42.3%

Most occurring characters

ValueCountFrequency (%)
0 842
57.7%
1 618
42.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 842
57.7%
1 618
42.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 842
57.7%
1 618
42.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 842
57.7%
1 618
42.3%

BsmtQual_TA
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
811 
1
649 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 811
55.5%
1 649
44.5%

Length

2023-05-18T15:06:27.817636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:27.864666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 811
55.5%
1 649
44.5%

Most occurring characters

ValueCountFrequency (%)
0 811
55.5%
1 649
44.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 811
55.5%
1 649
44.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 811
55.5%
1 649
44.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 811
55.5%
1 649
44.5%

BsmtCond_Fa
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1415 
1
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1415
96.9%
1 45
 
3.1%

Length

2023-05-18T15:06:27.903324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:27.949885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1415
96.9%
1 45
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 1415
96.9%
1 45
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1415
96.9%
1 45
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1415
96.9%
1 45
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1415
96.9%
1 45
 
3.1%

BsmtCond_Gd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1395 
1
 
65

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1395
95.5%
1 65
 
4.5%

Length

2023-05-18T15:06:27.987707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:28.034662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1395
95.5%
1 65
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 1395
95.5%
1 65
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1395
95.5%
1 65
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1395
95.5%
1 65
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1395
95.5%
1 65
 
4.5%

BsmtCond_Po
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1458 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Length

2023-05-18T15:06:28.074018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:28.121286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

BsmtCond_TA
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1311 
0
149 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 1311
89.8%
0 149
 
10.2%

Length

2023-05-18T15:06:28.159398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:28.205963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1311
89.8%
0 149
 
10.2%

Most occurring characters

ValueCountFrequency (%)
1 1311
89.8%
0 149
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1311
89.8%
0 149
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1311
89.8%
0 149
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1311
89.8%
0 149
 
10.2%

BsmtExposure_Av
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1239 
1
221 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1239
84.9%
1 221
 
15.1%

Length

2023-05-18T15:06:28.245250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:28.292113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1239
84.9%
1 221
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 1239
84.9%
1 221
 
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1239
84.9%
1 221
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1239
84.9%
1 221
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1239
84.9%
1 221
 
15.1%

BsmtExposure_Gd
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1326 
1
134 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1326
90.8%
1 134
 
9.2%

Length

2023-05-18T15:06:28.333954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:28.382895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1326
90.8%
1 134
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 1326
90.8%
1 134
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1326
90.8%
1 134
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1326
90.8%
1 134
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1326
90.8%
1 134
 
9.2%

BsmtExposure_Mn
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1346 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1346
92.2%
1 114
 
7.8%

Length

2023-05-18T15:06:28.422361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:28.471422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1346
92.2%
1 114
 
7.8%

Most occurring characters

ValueCountFrequency (%)
0 1346
92.2%
1 114
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1346
92.2%
1 114
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1346
92.2%
1 114
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1346
92.2%
1 114
 
7.8%

BsmtExposure_No
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
953 
0
507 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 953
65.3%
0 507
34.7%

Length

2023-05-18T15:06:28.510659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:28.558914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 953
65.3%
0 507
34.7%

Most occurring characters

ValueCountFrequency (%)
1 953
65.3%
0 507
34.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 953
65.3%
0 507
34.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 953
65.3%
0 507
34.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 953
65.3%
0 507
34.7%

BsmtFinType1_ALQ
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1240 
1
220 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1240
84.9%
1 220
 
15.1%

Length

2023-05-18T15:06:28.598487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:28.645519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1240
84.9%
1 220
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 1240
84.9%
1 220
 
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1240
84.9%
1 220
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1240
84.9%
1 220
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1240
84.9%
1 220
 
15.1%

BsmtFinType1_BLQ
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1312 
1
148 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1312
89.9%
1 148
 
10.1%

Length

2023-05-18T15:06:28.684742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:28.732113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1312
89.9%
1 148
 
10.1%

Most occurring characters

ValueCountFrequency (%)
0 1312
89.9%
1 148
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1312
89.9%
1 148
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1312
89.9%
1 148
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1312
89.9%
1 148
 
10.1%

BsmtFinType1_GLQ
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1042 
1
418 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1042
71.4%
1 418
28.6%

Length

2023-05-18T15:06:28.772939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:28.819118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1042
71.4%
1 418
28.6%

Most occurring characters

ValueCountFrequency (%)
0 1042
71.4%
1 418
28.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1042
71.4%
1 418
28.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1042
71.4%
1 418
28.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1042
71.4%
1 418
28.6%

BsmtFinType1_LwQ
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1386 
1
 
74

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1386
94.9%
1 74
 
5.1%

Length

2023-05-18T15:06:28.859217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:28.905133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1386
94.9%
1 74
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 1386
94.9%
1 74
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1386
94.9%
1 74
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1386
94.9%
1 74
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1386
94.9%
1 74
 
5.1%

BsmtFinType1_Rec
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1327 
1
133 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1327
90.9%
1 133
 
9.1%

Length

2023-05-18T15:06:28.943907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:28.990955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1327
90.9%
1 133
 
9.1%

Most occurring characters

ValueCountFrequency (%)
0 1327
90.9%
1 133
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1327
90.9%
1 133
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1327
90.9%
1 133
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1327
90.9%
1 133
 
9.1%

BsmtFinType1_Unf
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1030 
1
430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1030
70.5%
1 430
29.5%

Length

2023-05-18T15:06:29.030511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:29.076936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1030
70.5%
1 430
29.5%

Most occurring characters

ValueCountFrequency (%)
0 1030
70.5%
1 430
29.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1030
70.5%
1 430
29.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1030
70.5%
1 430
29.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1030
70.5%
1 430
29.5%

BsmtFinType2_ALQ
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1441 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1441
98.7%
1 19
 
1.3%

Length

2023-05-18T15:06:29.116243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:29.162419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1441
98.7%
1 19
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 1441
98.7%
1 19
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1441
98.7%
1 19
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1441
98.7%
1 19
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1441
98.7%
1 19
 
1.3%

BsmtFinType2_BLQ
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1427 
1
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1427
97.7%
1 33
 
2.3%

Length

2023-05-18T15:06:29.200577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:29.246596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1427
97.7%
1 33
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1427
97.7%
1 33
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1427
97.7%
1 33
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1427
97.7%
1 33
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1427
97.7%
1 33
 
2.3%

BsmtFinType2_GLQ
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1446 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Length

2023-05-18T15:06:29.284876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:29.331374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

BsmtFinType2_LwQ
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1414 
1
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1414
96.8%
1 46
 
3.2%

Length

2023-05-18T15:06:29.371058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:29.416616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1414
96.8%
1 46
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 1414
96.8%
1 46
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1414
96.8%
1 46
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1414
96.8%
1 46
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1414
96.8%
1 46
 
3.2%

BsmtFinType2_Rec
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1406 
1
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1406
96.3%
1 54
 
3.7%

Length

2023-05-18T15:06:29.455473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:29.503094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1406
96.3%
1 54
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 1406
96.3%
1 54
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1406
96.3%
1 54
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1406
96.3%
1 54
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1406
96.3%
1 54
 
3.7%

BsmtFinType2_Unf
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1256 
0
204 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1256
86.0%
0 204
 
14.0%

Length

2023-05-18T15:06:29.543810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:29.590886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1256
86.0%
0 204
 
14.0%

Most occurring characters

ValueCountFrequency (%)
1 1256
86.0%
0 204
 
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1256
86.0%
0 204
 
14.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1256
86.0%
0 204
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1256
86.0%
0 204
 
14.0%

Heating_Floor
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Length

2023-05-18T15:06:29.630658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:29.677098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Heating_GasA
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1428 
0
 
32

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1428
97.8%
0 32
 
2.2%

Length

2023-05-18T15:06:29.716497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:29.762909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1428
97.8%
0 32
 
2.2%

Most occurring characters

ValueCountFrequency (%)
1 1428
97.8%
0 32
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1428
97.8%
0 32
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1428
97.8%
0 32
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1428
97.8%
0 32
 
2.2%

Heating_GasW
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1442 
1
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1442
98.8%
1 18
 
1.2%

Length

2023-05-18T15:06:29.800680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:29.847860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1442
98.8%
1 18
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 1442
98.8%
1 18
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1442
98.8%
1 18
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1442
98.8%
1 18
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1442
98.8%
1 18
 
1.2%

Heating_Grav
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1453 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Length

2023-05-18T15:06:29.886530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:29.933024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Heating_OthW
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1458 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Length

2023-05-18T15:06:29.971254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:30.018692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Heating_Wall
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1456 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Length

2023-05-18T15:06:30.056857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:30.102745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

HeatingQC_Ex
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
741 
0
719 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 741
50.8%
0 719
49.2%

Length

2023-05-18T15:06:30.141631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:30.191964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 741
50.8%
0 719
49.2%

Most occurring characters

ValueCountFrequency (%)
1 741
50.8%
0 719
49.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 741
50.8%
0 719
49.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 741
50.8%
0 719
49.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 741
50.8%
0 719
49.2%

HeatingQC_Fa
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1411 
1
 
49

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1411
96.6%
1 49
 
3.4%

Length

2023-05-18T15:06:30.231185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:30.277881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1411
96.6%
1 49
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 1411
96.6%
1 49
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1411
96.6%
1 49
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1411
96.6%
1 49
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1411
96.6%
1 49
 
3.4%

HeatingQC_Gd
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1219 
1
241 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1219
83.5%
1 241
 
16.5%

Length

2023-05-18T15:06:30.315564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:30.363699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1219
83.5%
1 241
 
16.5%

Most occurring characters

ValueCountFrequency (%)
0 1219
83.5%
1 241
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1219
83.5%
1 241
 
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1219
83.5%
1 241
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1219
83.5%
1 241
 
16.5%

HeatingQC_Po
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Length

2023-05-18T15:06:30.403892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:30.451833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

HeatingQC_TA
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1032 
1
428 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1032
70.7%
1 428
29.3%

Length

2023-05-18T15:06:30.489919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:30.537625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1032
70.7%
1 428
29.3%

Most occurring characters

ValueCountFrequency (%)
0 1032
70.7%
1 428
29.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1032
70.7%
1 428
29.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1032
70.7%
1 428
29.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1032
70.7%
1 428
29.3%

CentralAir_N
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1365 
1
 
95

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1365
93.5%
1 95
 
6.5%

Length

2023-05-18T15:06:30.576811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:30.624647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1365
93.5%
1 95
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 1365
93.5%
1 95
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1365
93.5%
1 95
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1365
93.5%
1 95
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1365
93.5%
1 95
 
6.5%

CentralAir_Y
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1365 
0
 
95

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1365
93.5%
0 95
 
6.5%

Length

2023-05-18T15:06:30.663634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:30.710167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1365
93.5%
0 95
 
6.5%

Most occurring characters

ValueCountFrequency (%)
1 1365
93.5%
0 95
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1365
93.5%
0 95
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1365
93.5%
0 95
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1365
93.5%
0 95
 
6.5%

Electrical_FuseA
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1366 
1
 
94

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1366
93.6%
1 94
 
6.4%

Length

2023-05-18T15:06:30.748057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:30.795252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1366
93.6%
1 94
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 1366
93.6%
1 94
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1366
93.6%
1 94
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1366
93.6%
1 94
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1366
93.6%
1 94
 
6.4%

Electrical_FuseF
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1433 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1433
98.2%
1 27
 
1.8%

Length

2023-05-18T15:06:30.834221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:30.881178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1433
98.2%
1 27
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 1433
98.2%
1 27
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1433
98.2%
1 27
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1433
98.2%
1 27
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1433
98.2%
1 27
 
1.8%

Electrical_FuseP
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1457 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Length

2023-05-18T15:06:30.919611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:30.965895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Electrical_Mix
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Length

2023-05-18T15:06:31.004334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:31.050044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Electrical_SBrkr
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1334 
0
 
126

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1334
91.4%
0 126
 
8.6%

Length

2023-05-18T15:06:31.088493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:31.134423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1334
91.4%
0 126
 
8.6%

Most occurring characters

ValueCountFrequency (%)
1 1334
91.4%
0 126
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1334
91.4%
0 126
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1334
91.4%
0 126
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1334
91.4%
0 126
 
8.6%

KitchenQual_Ex
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1360 
1
 
100

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1360
93.2%
1 100
 
6.8%

Length

2023-05-18T15:06:31.175434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:31.221359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1360
93.2%
1 100
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 1360
93.2%
1 100
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1360
93.2%
1 100
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1360
93.2%
1 100
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1360
93.2%
1 100
 
6.8%

KitchenQual_Fa
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1421 
1
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1421
97.3%
1 39
 
2.7%

Length

2023-05-18T15:06:31.260645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:31.307601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1421
97.3%
1 39
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 1421
97.3%
1 39
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1421
97.3%
1 39
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1421
97.3%
1 39
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1421
97.3%
1 39
 
2.7%

KitchenQual_Gd
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
874 
1
586 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 874
59.9%
1 586
40.1%

Length

2023-05-18T15:06:31.346737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:31.394664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 874
59.9%
1 586
40.1%

Most occurring characters

ValueCountFrequency (%)
0 874
59.9%
1 586
40.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 874
59.9%
1 586
40.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 874
59.9%
1 586
40.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 874
59.9%
1 586
40.1%

KitchenQual_TA
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
735 
0
725 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 735
50.3%
0 725
49.7%

Length

2023-05-18T15:06:31.958751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:32.006911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 735
50.3%
0 725
49.7%

Most occurring characters

ValueCountFrequency (%)
1 735
50.3%
0 725
49.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 735
50.3%
0 725
49.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 735
50.3%
0 725
49.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 735
50.3%
0 725
49.7%

Functional_Maj1
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1446 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Length

2023-05-18T15:06:32.047089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:32.093469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Functional_Maj2
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1455 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Length

2023-05-18T15:06:32.133115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:32.179371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Functional_Min1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1429 
1
 
31

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1429
97.9%
1 31
 
2.1%

Length

2023-05-18T15:06:32.217914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:32.263743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1429
97.9%
1 31
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1429
97.9%
1 31
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1429
97.9%
1 31
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1429
97.9%
1 31
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1429
97.9%
1 31
 
2.1%

Functional_Min2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1426 
1
 
34

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1426
97.7%
1 34
 
2.3%

Length

2023-05-18T15:06:32.302247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:32.347799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1426
97.7%
1 34
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1426
97.7%
1 34
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1426
97.7%
1 34
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1426
97.7%
1 34
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1426
97.7%
1 34
 
2.3%

Functional_Mod
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1445 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1445
99.0%
1 15
 
1.0%

Length

2023-05-18T15:06:32.386847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:32.433337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1445
99.0%
1 15
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 1445
99.0%
1 15
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1445
99.0%
1 15
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1445
99.0%
1 15
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1445
99.0%
1 15
 
1.0%

Functional_Sev
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Length

2023-05-18T15:06:32.472549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:32.518511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1459
99.9%
1 1
 
0.1%

Functional_Typ
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1360 
0
 
100

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1360
93.2%
0 100
 
6.8%

Length

2023-05-18T15:06:32.557392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:32.603222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1360
93.2%
0 100
 
6.8%

Most occurring characters

ValueCountFrequency (%)
1 1360
93.2%
0 100
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1360
93.2%
0 100
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1360
93.2%
0 100
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1360
93.2%
0 100
 
6.8%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1454 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1454
99.6%
1 6
 
0.4%

Length

2023-05-18T15:06:32.641492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:32.687380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1454
99.6%
1 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1454
99.6%
1 6
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1454
99.6%
1 6
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1454
99.6%
1 6
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1454
99.6%
1 6
 
0.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
870 
0
590 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 870
59.6%
0 590
40.4%

Length

2023-05-18T15:06:32.727338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:32.774370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 870
59.6%
0 590
40.4%

Most occurring characters

ValueCountFrequency (%)
1 870
59.6%
0 590
40.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 870
59.6%
0 590
40.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 870
59.6%
0 590
40.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 870
59.6%
0 590
40.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1441 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1441
98.7%
1 19
 
1.3%

Length

2023-05-18T15:06:32.815142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:32.861005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1441
98.7%
1 19
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 1441
98.7%
1 19
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1441
98.7%
1 19
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1441
98.7%
1 19
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1441
98.7%
1 19
 
1.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1372 
1
 
88

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1372
94.0%
1 88
 
6.0%

Length

2023-05-18T15:06:32.899510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:32.946575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1372
94.0%
1 88
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 1372
94.0%
1 88
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1372
94.0%
1 88
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1372
94.0%
1 88
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1372
94.0%
1 88
 
6.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1451 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Length

2023-05-18T15:06:32.985516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:33.033383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1073 
1
387 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1073
73.5%
1 387
 
26.5%

Length

2023-05-18T15:06:33.073106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:33.119782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1073
73.5%
1 387
 
26.5%

Most occurring characters

ValueCountFrequency (%)
0 1073
73.5%
1 387
 
26.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1073
73.5%
1 387
 
26.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1073
73.5%
1 387
 
26.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1073
73.5%
1 387
 
26.5%

GarageFinish_Fin
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1108 
1
352 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1108
75.9%
1 352
 
24.1%

Length

2023-05-18T15:06:33.159246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:33.205539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1108
75.9%
1 352
 
24.1%

Most occurring characters

ValueCountFrequency (%)
0 1108
75.9%
1 352
 
24.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1108
75.9%
1 352
 
24.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1108
75.9%
1 352
 
24.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1108
75.9%
1 352
 
24.1%

GarageFinish_RFn
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1038 
1
422 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1038
71.1%
1 422
28.9%

Length

2023-05-18T15:06:33.244860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:33.291030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1038
71.1%
1 422
28.9%

Most occurring characters

ValueCountFrequency (%)
0 1038
71.1%
1 422
28.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1038
71.1%
1 422
28.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1038
71.1%
1 422
28.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1038
71.1%
1 422
28.9%

GarageFinish_Unf
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
855 
1
605 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 855
58.6%
1 605
41.4%

Length

2023-05-18T15:06:33.330490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:33.376327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 855
58.6%
1 605
41.4%

Most occurring characters

ValueCountFrequency (%)
0 855
58.6%
1 605
41.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 855
58.6%
1 605
41.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 855
58.6%
1 605
41.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 855
58.6%
1 605
41.4%

GarageQual_Ex
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1457 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Length

2023-05-18T15:06:33.415621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:33.461848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

GarageQual_Fa
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1412 
1
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1412
96.7%
1 48
 
3.3%

Length

2023-05-18T15:06:33.499868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:33.547447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1412
96.7%
1 48
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 1412
96.7%
1 48
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1412
96.7%
1 48
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1412
96.7%
1 48
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1412
96.7%
1 48
 
3.3%

GarageQual_Gd
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1446 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Length

2023-05-18T15:06:33.586254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:33.632707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1446
99.0%
1 14
 
1.0%

GarageQual_Po
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1457 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Length

2023-05-18T15:06:33.670207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:33.716723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

GarageQual_TA
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1311 
0
149 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1311
89.8%
0 149
 
10.2%

Length

2023-05-18T15:06:33.754547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:33.801624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1311
89.8%
0 149
 
10.2%

Most occurring characters

ValueCountFrequency (%)
1 1311
89.8%
0 149
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1311
89.8%
0 149
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1311
89.8%
0 149
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1311
89.8%
0 149
 
10.2%

GarageCond_Ex
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1458 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Length

2023-05-18T15:06:33.840758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:33.887227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

GarageCond_Fa
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1425 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1425
97.6%
1 35
 
2.4%

Length

2023-05-18T15:06:33.925161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:33.970943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1425
97.6%
1 35
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 1425
97.6%
1 35
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1425
97.6%
1 35
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1425
97.6%
1 35
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1425
97.6%
1 35
 
2.4%

GarageCond_Gd
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1451 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Length

2023-05-18T15:06:34.010490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:34.056697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

GarageCond_Po
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1453 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Length

2023-05-18T15:06:34.094631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:34.140968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1453
99.5%
1 7
 
0.5%

GarageCond_TA
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1326 
0
134 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1326
90.8%
0 134
 
9.2%

Length

2023-05-18T15:06:34.178566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:34.225321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1326
90.8%
0 134
 
9.2%

Most occurring characters

ValueCountFrequency (%)
1 1326
90.8%
0 134
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1326
90.8%
0 134
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1326
90.8%
0 134
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1326
90.8%
0 134
 
9.2%

SaleType_COD
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1417 
1
 
43

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1417
97.1%
1 43
 
2.9%

Length

2023-05-18T15:06:34.264418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:34.312342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1417
97.1%
1 43
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 1417
97.1%
1 43
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1417
97.1%
1 43
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1417
97.1%
1 43
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1417
97.1%
1 43
 
2.9%

SaleType_CWD
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1456 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Length

2023-05-18T15:06:34.350416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:34.397089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

SaleType_Con
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1458 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Length

2023-05-18T15:06:34.438448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:34.486142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1458
99.9%
1 2
 
0.1%

SaleType_ConLD
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1451 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Length

2023-05-18T15:06:34.525651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:34.571577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1451
99.4%
1 9
 
0.6%

SaleType_ConLI
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1455 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Length

2023-05-18T15:06:34.610131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:34.655848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

SaleType_ConLw
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1455 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Length

2023-05-18T15:06:34.693878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:34.739640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1455
99.7%
1 5
 
0.3%

SaleType_New
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1338 
1
 
122

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1338
91.6%
1 122
 
8.4%

Length

2023-05-18T15:06:34.777842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:34.824473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1338
91.6%
1 122
 
8.4%

Most occurring characters

ValueCountFrequency (%)
0 1338
91.6%
1 122
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1338
91.6%
1 122
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1338
91.6%
1 122
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1338
91.6%
1 122
 
8.4%

SaleType_Oth
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1457 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Length

2023-05-18T15:06:34.865755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:34.911701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1457
99.8%
1 3
 
0.2%

SaleType_WD
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1267 
0
193 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1267
86.8%
0 193
 
13.2%

Length

2023-05-18T15:06:34.950148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:34.996099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1267
86.8%
0 193
 
13.2%

Most occurring characters

ValueCountFrequency (%)
1 1267
86.8%
0 193
 
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1267
86.8%
0 193
 
13.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1267
86.8%
0 193
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1267
86.8%
0 193
 
13.2%

SaleCondition_Abnorml
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1359 
1
 
101

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1359
93.1%
1 101
 
6.9%

Length

2023-05-18T15:06:35.036421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:35.082328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1359
93.1%
1 101
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0 1359
93.1%
1 101
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1359
93.1%
1 101
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1359
93.1%
1 101
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1359
93.1%
1 101
 
6.9%

SaleCondition_AdjLand
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1456 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Length

2023-05-18T15:06:35.120850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:35.166930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1456
99.7%
1 4
 
0.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1448 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1448
99.2%
1 12
 
0.8%

Length

2023-05-18T15:06:35.206616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:35.252896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1448
99.2%
1 12
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 1448
99.2%
1 12
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1448
99.2%
1 12
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1448
99.2%
1 12
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1448
99.2%
1 12
 
0.8%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1440 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1440
98.6%
1 20
 
1.4%

Length

2023-05-18T15:06:35.291368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:35.337305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1440
98.6%
1 20
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 1440
98.6%
1 20
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1440
98.6%
1 20
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1440
98.6%
1 20
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1440
98.6%
1 20
 
1.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1198 
0
262 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 1198
82.1%
0 262
 
17.9%

Length

2023-05-18T15:06:35.375030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:35.422421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1198
82.1%
0 262
 
17.9%

Most occurring characters

ValueCountFrequency (%)
1 1198
82.1%
0 262
 
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1198
82.1%
0 262
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1198
82.1%
0 262
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1198
82.1%
0 262
 
17.9%

SaleCondition_Partial
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1335 
1
 
125

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1335
91.4%
1 125
 
8.6%

Length

2023-05-18T15:06:35.461214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:35.508665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1335
91.4%
1 125
 
8.6%

Most occurring characters

ValueCountFrequency (%)
0 1335
91.4%
1 125
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1335
91.4%
1 125
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1335
91.4%
1 125
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1335
91.4%
1 125
 
8.6%

LotShape1_Irr
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
925 
1
535 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 925
63.4%
1 535
36.6%

Length

2023-05-18T15:06:35.549622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:35.596798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 925
63.4%
1 535
36.6%

Most occurring characters

ValueCountFrequency (%)
0 925
63.4%
1 535
36.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 925
63.4%
1 535
36.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 925
63.4%
1 535
36.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 925
63.4%
1 535
36.6%

LotShape1_Reg
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
925 
0
535 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 925
63.4%
0 535
36.6%

Length

2023-05-18T15:06:35.635513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-18T15:06:35.681731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 925
63.4%
0 535
36.6%

Most occurring characters

ValueCountFrequency (%)
1 925
63.4%
0 535
36.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 925
63.4%
0 535
36.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 925
63.4%
0 535
36.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 925
63.4%
0 535
36.6%

Interactions

2023-05-18T15:06:11.331096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:38.159013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:39.545564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:40.904418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:42.137764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:43.481769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:44.806122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:46.251630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:47.553261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:48.986838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:50.306937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:51.540948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:52.829825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:54.238168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:55.460928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:56.746510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:57.989139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:59.562393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:00.851057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:02.125663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:03.436038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:04.966516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:06.235652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:07.505727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:08.762659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:10.023680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:11.381488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:38.214290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:39.595479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:40.952358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:42.184939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:43.535524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:44.856464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:46.303858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:47.602874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:49.037729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:50.354670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:51.592673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:53.076761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:54.286482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:55.511158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:56.796327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:58.040880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:59.611982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:00.902229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:02.178294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:03.484050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:05.017777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:06.284233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:07.555750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:08.811855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:10.075240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:11.429727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:38.264286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:39.641787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:41.000192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:42.232294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:43.585031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:44.905220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:46.353689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:47.652247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:49.089538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:50.401352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:51.641003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:53.124109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:54.333861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:55.558950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:56.844064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:58.335719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:59.660928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:00.951228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:02.229599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:03.531075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:05.066683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:06.331889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:07.602968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:08.859982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:10.124204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:11.475051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:38.312327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:39.687684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:41.043889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:42.276533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:43.633461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:44.953337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:46.401450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:47.698110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:49.137547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:50.445606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:51.687758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:53.168974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-18T15:06:08.381609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:09.638355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-18T15:06:05.893695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:07.170622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:08.432277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:09.689770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:10.985056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:12.305333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:39.249093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:40.619900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:41.853231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:43.201899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:44.499550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:45.951348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:47.254629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:48.522931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:50.002495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:51.256979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:52.528839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:53.960447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:55.177855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:56.453118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:57.705899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:59.256836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:00.548807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:01.834495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:03.137646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:04.394872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:05.939413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:07.216677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:08.477352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:09.734845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:11.032287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:12.353056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:39.297515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:40.666096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:41.899768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:43.248612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:44.550675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:46.001564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:47.303167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:48.571888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:50.053378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:51.304460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:52.577401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:54.005562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:55.224842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:56.501321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:57.752406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:59.308078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:00.598825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:01.882225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:03.186254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:04.441470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:05.986882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:07.264771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:08.525057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:09.785697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:11.081716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:12.759330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:39.345946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:40.713860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:41.947773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:43.294474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:44.600322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:46.050846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:47.352495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:48.620034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:50.103861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:51.351361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:52.627665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:54.052103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:55.270599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:56.549197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:57.798993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:59.357704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:00.648292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:01.930952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:03.236643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:04.487683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:06.033995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:07.312770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:08.572070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:09.832127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:11.131031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:12.807457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:39.396134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:40.760774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:41.994082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:43.340826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:44.650547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:46.100896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:47.401011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:48.836985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:50.152141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:51.398141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:52.677161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:54.097512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:55.316800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:56.597103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:57.846087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:59.409281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:00.697452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:01.979537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:03.285379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:04.824205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:06.082721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:07.359371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:08.619145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:09.879854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:11.179127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:12.854785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:39.444653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:40.807494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:42.041896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:43.386232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:44.701090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:46.149480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:47.450393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:48.885719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:50.203308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:51.444441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:52.727531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:54.143577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:55.363587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:56.645648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:57.892834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:59.459393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:00.749898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:02.026935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:03.334722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:04.870570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:06.136301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:07.407170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:08.665553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:09.927341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:11.228279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:12.908705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:39.497138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:40.857722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:42.091139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:43.436995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:44.756769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:46.202229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:47.503480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:48.937324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:50.257365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:51.494206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:52.780546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:54.193155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:55.414685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:56.697400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:57.942475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:05:59.512988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:00.803615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:02.078060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:03.387719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:04.920120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:06.187378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:07.457360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:08.716067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:09.977610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T15:06:11.281265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-18T15:06:35.911068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
MSSubClassLotFrontageLotAreaOverallQualOverallCondYearBuiltYearRemodAddBsmtFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSF1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBedroomAbvGrTotRmsAbvGrdGarageYrBltGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMoSoldBsmtFullBathBsmtHalfBathFullBathHalfBathKitchenAbvGrFireplacesGarageCarsYrSoldMSZoning_C (all)MSZoning_FVMSZoning_RHMSZoning_RLMSZoning_RMLandContour_BnkLandContour_HLSLandContour_LowLandContour_LvlLotConfig_CornerLotConfig_CulDSacLotConfig_FR2LotConfig_FR3LotConfig_InsideNeighborhood_BlmngtnNeighborhood_BluesteNeighborhood_BrDaleNeighborhood_BrkSideNeighborhood_ClearCrNeighborhood_CollgCrNeighborhood_CrawforNeighborhood_EdwardsNeighborhood_GilbertNeighborhood_IDOTRRNeighborhood_MeadowVNeighborhood_MitchelNeighborhood_NAmesNeighborhood_NPkVillNeighborhood_NWAmesNeighborhood_NoRidgeNeighborhood_NridgHtNeighborhood_OldTownNeighborhood_SWISUNeighborhood_SawyerNeighborhood_SawyerWNeighborhood_SomerstNeighborhood_StoneBrNeighborhood_TimberNeighborhood_VeenkerBldgType_1FamBldgType_2fmConBldgType_DuplexBldgType_TwnhsBldgType_TwnhsEHouseStyle_1.5FinHouseStyle_1.5UnfHouseStyle_1StoryHouseStyle_2.5FinHouseStyle_2.5UnfHouseStyle_2StoryHouseStyle_SFoyerHouseStyle_SLvlRoofStyle_FlatRoofStyle_GableRoofStyle_GambrelRoofStyle_HipRoofStyle_MansardRoofStyle_ShedExterior1st_AsbShngExterior1st_AsphShnExterior1st_BrkCommExterior1st_BrkFaceExterior1st_CBlockExterior1st_CemntBdExterior1st_HdBoardExterior1st_ImStuccExterior1st_MetalSdExterior1st_PlywoodExterior1st_StoneExterior1st_StuccoExterior1st_VinylSdExterior1st_Wd SdngExterior1st_WdShingExterior2nd_AsbShngExterior2nd_AsphShnExterior2nd_Brk CmnExterior2nd_BrkFaceExterior2nd_CBlockExterior2nd_CmentBdExterior2nd_HdBoardExterior2nd_ImStuccExterior2nd_MetalSdExterior2nd_OtherExterior2nd_PlywoodExterior2nd_StoneExterior2nd_StuccoExterior2nd_VinylSdExterior2nd_Wd SdngExterior2nd_Wd ShngMasVnrType_BrkCmnMasVnrType_BrkFaceMasVnrType_NoneMasVnrType_StoneExterQual_ExExterQual_FaExterQual_GdExterQual_TAExterCond_ExExterCond_FaExterCond_GdExterCond_PoExterCond_TAFoundation_BrkTilFoundation_CBlockFoundation_PConcFoundation_SlabFoundation_StoneFoundation_WoodBsmtQual_ExBsmtQual_FaBsmtQual_GdBsmtQual_TABsmtCond_FaBsmtCond_GdBsmtCond_PoBsmtCond_TABsmtExposure_AvBsmtExposure_GdBsmtExposure_MnBsmtExposure_NoBsmtFinType1_ALQBsmtFinType1_BLQBsmtFinType1_GLQBsmtFinType1_LwQBsmtFinType1_RecBsmtFinType1_UnfBsmtFinType2_ALQBsmtFinType2_BLQBsmtFinType2_GLQBsmtFinType2_LwQBsmtFinType2_RecBsmtFinType2_UnfHeating_FloorHeating_GasAHeating_GasWHeating_GravHeating_OthWHeating_WallHeatingQC_ExHeatingQC_FaHeatingQC_GdHeatingQC_PoHeatingQC_TACentralAir_NCentralAir_YElectrical_FuseAElectrical_FuseFElectrical_FusePElectrical_MixElectrical_SBrkrKitchenQual_ExKitchenQual_FaKitchenQual_GdKitchenQual_TAFunctional_Maj1Functional_Maj2Functional_Min1Functional_Min2Functional_ModFunctional_SevFunctional_TypGarageType_2TypesGarageType_AttchdGarageType_BasmentGarageType_BuiltInGarageType_CarPortGarageType_DetchdGarageFinish_FinGarageFinish_RFnGarageFinish_UnfGarageQual_ExGarageQual_FaGarageQual_GdGarageQual_PoGarageQual_TAGarageCond_ExGarageCond_FaGarageCond_GdGarageCond_PoGarageCond_TASaleType_CODSaleType_CWDSaleType_ConSaleType_ConLDSaleType_ConLISaleType_ConLwSaleType_NewSaleType_OthSaleType_WDSaleCondition_AbnormlSaleCondition_AdjLandSaleCondition_AllocaSaleCondition_FamilySaleCondition_NormalSaleCondition_PartialLotShape1_IrrLotShape1_Reg
MSSubClass1.000-0.269-0.2700.108-0.0720.0360.007-0.108-0.084-0.118-0.319-0.2780.4880.0760.2040.0690.1660.080-0.0470.0230.0320.011-0.036-0.0220.0330.0180.2090.0860.2490.5120.4760.1930.2430.0000.0620.3310.0960.4310.3770.1420.0490.0000.0890.0800.0910.0000.0210.0920.3980.1600.4910.2630.0480.1760.0640.1550.2860.1620.3460.1590.2740.2320.1160.2110.2040.2460.1750.1600.0890.2780.2080.0610.0710.9880.8460.9980.6330.8580.8910.2290.9600.2030.2750.9610.4060.7290.0090.2070.0830.2330.0360.0810.1120.1170.0000.0800.0280.1850.1560.0000.2080.1810.0000.0350.2990.2170.0630.1170.0640.2200.0590.0280.1870.1580.0000.2140.0000.1770.0990.0460.3130.2340.0000.0000.2100.2600.1510.0970.1120.3820.3940.0000.2050.1240.0000.1730.3380.3450.4340.2610.0280.0490.1820.1020.3770.3980.1140.0650.0000.1400.2780.1410.0850.2940.1610.1470.3020.0910.1410.2140.0490.0170.0530.0590.0630.1680.0000.1460.1290.0650.0230.1930.3160.0140.1380.0280.2560.2540.2540.1760.1360.0920.0000.2240.1310.1080.3250.3570.1410.0000.0690.1230.0720.0000.1380.0990.4100.1400.3120.1670.3830.2950.2570.3940.0000.1100.0740.0640.2530.0000.0260.1050.0000.2350.0820.0160.0310.1420.0000.0000.1560.0420.1160.0290.1340.2620.0390.0680.1570.2450.245
LotFrontage-0.2691.0000.5700.245-0.0760.1920.1050.1520.0500.0990.3540.3910.054-0.0330.3430.2900.3250.0990.3520.1080.163-0.0990.0500.0400.0780.0220.1370.0000.1210.0000.0110.2120.1790.0090.0000.1000.0000.3680.3540.1520.1010.0470.1480.2280.1090.0000.0420.1320.0900.0650.2530.0910.0500.0930.0000.0760.1720.0530.2230.0000.1460.0730.1180.1600.2120.0770.0350.0720.0000.0760.0590.0730.0000.4400.0000.0000.3910.4060.0830.0000.0000.0000.0000.0520.0000.0150.1850.1580.0000.1500.0610.0000.0000.0000.0000.0540.0000.1650.0000.0000.1070.0000.0000.1670.0670.1260.0000.0000.0000.0960.0220.0000.1620.0000.0930.1110.0000.0520.0000.1680.0760.1320.0040.0000.1320.2190.1650.1780.0300.1210.1640.0000.0000.0000.0000.0220.0800.0590.0830.0290.0000.1800.2270.0000.0000.0730.0420.0000.0000.0620.0000.1560.0090.1000.0000.0000.0970.0640.0420.0000.0000.0000.0870.0470.0000.0000.0000.0950.0440.1080.0000.0000.0690.0900.0000.0090.0000.0000.0000.0480.0000.0000.0000.0430.1820.0000.0140.1150.0830.0320.0000.0000.0000.0000.0000.0000.1880.0690.0390.0000.2030.1060.1040.1670.0000.0000.0000.0000.0790.0000.0000.0000.0000.0850.0000.0000.0000.0000.0000.0000.1400.0000.1120.0260.0000.0000.0930.1140.1400.1170.117
LotArea-0.2700.5701.0000.233-0.0470.1030.0750.1720.0720.0780.3660.4440.119-0.0200.4490.3380.4060.0380.3670.1840.177-0.0670.0620.0920.0840.0060.2110.0000.0980.0000.0000.1600.0110.0000.0000.0000.0000.0350.0000.1070.1030.4130.3040.0000.1840.0000.0000.0820.0000.0000.0000.0000.4120.0000.0000.0000.0000.0000.0000.0730.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.1960.0790.0000.1200.0000.0000.0000.0990.0000.0120.0000.0000.0000.0000.0000.2280.0970.0000.0390.0000.1250.0000.0000.0000.1250.0000.0000.0000.0000.0000.1330.0000.0190.0670.0370.0000.0000.0000.0000.1890.0000.0000.0000.0000.0000.0000.1260.0550.0120.0640.0000.0850.1730.0000.0000.0610.0580.0000.0000.0000.0000.0000.0600.0000.0400.0000.0220.0000.0000.0000.0000.0000.0000.0300.0240.0000.0000.0000.0000.0000.2620.0000.1270.0810.0000.0110.0000.0470.0000.0000.1030.0000.0000.1470.0490.0000.1640.2260.0000.0000.0000.0000.0000.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1010.0000.0690.0000.0720.1470.0000.0000.0000.0000.0000.0840.0000.0000.0930.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1280.128
OverallQual0.1080.2450.2331.000-0.1780.6470.5580.133-0.1180.2730.4600.4090.290-0.0340.6030.1220.4280.5940.5420.2590.435-0.1620.0330.0460.0570.0610.0660.0620.4040.2250.1060.2670.4020.0000.2510.2100.0100.1440.2040.1020.1300.2270.1050.0460.0310.0000.0270.0320.1480.0000.0850.1660.0000.2190.0170.2340.1810.1940.1970.0730.2840.1090.1580.2920.4910.1650.1080.2440.0850.2600.2560.1540.0000.0370.1180.1700.0820.1470.1730.0000.2230.0360.0280.3350.0750.0930.0000.2590.0000.2860.0000.0000.2870.0000.1370.0000.0420.2040.1510.0000.1990.1260.0000.1540.4010.1890.0290.2850.0000.0780.0000.0420.2120.1250.0330.1890.0000.1360.0840.0970.4010.1860.0000.0240.2480.3990.3720.6790.4290.6790.7220.1110.2970.0820.2080.1580.1600.4920.6090.2540.0000.0000.6560.1600.5310.5560.2050.0710.4940.2040.1500.2540.0000.2330.1940.1900.4390.1100.2020.1160.0000.1240.0000.0690.1020.2060.2080.2070.0000.2260.0000.2230.4870.2040.1320.0000.3780.3740.3740.2300.2320.0000.0000.2990.6510.3340.5590.6100.2300.0680.0950.1310.0000.0000.1820.0000.3840.0000.1930.1370.3490.4120.3090.4550.1100.1110.0000.0000.2980.0000.1930.0000.0000.3490.0500.0200.0000.1220.0770.2520.3510.0640.2480.0950.0000.0000.0000.1720.3460.2010.201
OverallCond-0.072-0.076-0.047-0.1781.000-0.417-0.041-0.0110.102-0.128-0.217-0.1670.0010.040-0.154-0.004-0.105-0.363-0.201-0.043-0.1330.1100.0320.075-0.006-0.0070.0000.1020.3090.0790.0730.1050.2190.0500.1740.1610.0490.1420.2220.0820.0000.1580.0840.0910.0000.0000.0000.0060.0610.0330.0000.1550.0000.1870.1820.2000.1580.1790.0710.0000.1530.0400.1520.0710.1950.2730.0590.0410.0780.1900.0980.0610.0000.1830.1380.1310.0000.1660.2480.0920.0830.1470.0360.1610.0000.0180.0000.0000.1380.0000.0000.0000.1460.0000.2710.0330.0000.0120.0760.0000.1560.0760.0000.1280.4060.2410.0340.1420.0000.1360.0000.0000.0280.0560.0000.1420.0000.0980.0680.1440.3980.2300.0280.0290.1630.2670.1960.1500.3250.4010.4320.3590.3380.3660.4410.3910.2930.3560.5160.1510.1030.0000.2380.2360.3690.4630.2610.0880.7710.2110.0940.1150.0450.1310.1730.1620.3160.0780.1240.0670.0730.0000.0000.0890.0840.1240.0000.1770.1410.0920.0000.1400.3040.1560.1700.0000.2230.3150.3150.2230.2140.0000.4410.2850.1250.2600.2910.3200.2810.2090.0000.1260.1750.0000.2410.1930.2520.0520.1150.0240.2700.2400.1690.3210.1140.1110.0650.2540.2290.0330.1360.0960.1560.2380.0330.0000.0000.1500.0000.0810.2390.0860.2020.1030.0630.0000.0000.1830.2380.1160.116
YearBuilt0.0360.1920.1030.647-0.4171.0000.6840.190-0.1120.1390.4270.2930.030-0.1460.288-0.0350.1770.8480.5280.2880.393-0.4090.022-0.0730.0090.0190.1440.0890.3510.2270.2140.1690.3400.0000.1520.3130.0730.4240.4690.2430.0860.1010.1600.0840.1690.1030.0000.0950.1420.0380.2350.3800.0850.3430.2500.1840.2610.3080.2430.1390.5770.1680.3670.3410.3440.5590.2760.3280.3340.3650.1520.1120.1300.2400.3260.1880.2040.2600.5000.1680.3170.2180.2920.3350.2040.1940.0850.1630.1660.1780.2470.0380.1620.0000.0000.1790.0370.1610.4500.0190.2430.4220.0000.2510.6880.4340.0980.1740.0000.0830.0970.0370.1660.4200.0800.2380.0000.3980.0510.2530.6820.4130.1340.1340.3230.4380.3520.2250.1340.6740.7200.2320.1960.2080.0580.2620.6980.7760.8280.1670.2400.1160.3860.3110.6520.7410.2860.0790.0940.1740.2540.1460.0450.2280.3540.2680.5370.1620.3260.3770.0440.1160.0320.1550.1850.3290.0450.2660.2200.1290.2290.1630.6080.1860.2170.0580.5230.4380.4380.3030.1950.0690.0370.3690.2590.2570.5360.6110.0000.0800.1210.1490.0670.0000.1990.0520.5100.0300.2390.0540.5200.4170.3380.5600.2280.3310.1600.1080.4210.0000.2690.1060.2120.3800.1240.0000.0000.0000.0000.0460.4390.0130.3060.1020.0500.0350.0140.2180.4340.2810.281
YearRemodAdd0.0070.1050.0750.558-0.0410.6841.0000.063-0.1260.1770.2990.2400.073-0.0650.282-0.0540.1980.6960.3980.2300.353-0.2350.052-0.0460.0030.0210.1220.0770.2700.2000.1140.1360.2760.0000.1340.2650.0650.2440.2690.1830.1080.0720.1370.0650.1370.0750.0180.1010.1450.1090.3620.2260.0550.2800.1040.1730.1910.2250.2430.2000.4480.2410.2910.3010.3420.2170.1380.2560.2430.3400.1540.1170.0880.2090.0900.2330.2320.1870.3600.1570.2380.0650.0290.2680.1680.1720.0670.1150.0000.1330.0000.1090.1800.0680.0350.1850.0000.1890.4120.0000.1800.4420.0000.1170.5440.2650.0730.1970.0910.1240.1160.0000.1990.3690.0520.1790.0000.3920.0310.1410.5450.2970.0590.1010.2340.3030.4050.2790.1590.5620.6140.0000.1720.0890.0680.0910.3500.5740.6080.1420.0000.0000.3740.1800.4690.5310.1320.1240.0340.1140.2110.1330.0280.1770.2600.2200.4000.1290.2680.2250.0000.0830.0230.1960.1090.2270.0000.2060.0840.1400.0350.0920.5760.2210.1910.0000.5190.3780.3780.3460.2660.0000.0000.4360.2960.2480.5590.6230.0000.0000.1080.0760.0000.0540.1120.0710.3560.0700.1980.0480.3680.3300.2450.4300.0000.1800.0690.0700.2190.0350.1500.0990.0900.2130.1720.0500.0000.0000.0000.0090.5640.0540.4210.1230.0000.1080.0450.3400.5600.2150.215
BsmtFinSF1-0.1080.1520.1720.133-0.0110.1900.0631.0000.050-0.5740.4100.323-0.191-0.0790.057-0.084-0.0500.0720.2440.1790.081-0.1480.0470.0720.058-0.0160.3970.0280.1580.0120.0000.2980.1760.0000.0000.0190.0000.2020.1820.1940.0460.1350.1220.0360.0920.0280.0000.0510.0000.0000.0330.0790.0000.0670.0530.1440.0700.0880.0000.0780.0930.0000.0850.1640.2100.1550.0540.0000.0000.0340.1620.1230.1000.0000.0000.0000.0190.0000.1430.0000.2180.0000.0000.1350.1190.0000.0000.2050.0000.2260.0000.0120.0320.0000.0000.0000.0000.1940.1040.0680.0490.0930.0120.1980.0000.0660.0460.0000.0000.0000.0000.0000.1870.0970.1150.0420.0680.0770.0000.1920.0000.0840.0200.0000.1320.2260.2370.3210.0480.1270.2150.0000.0510.0290.0000.0210.1870.1300.1740.0800.0000.0000.3950.0610.1060.1910.0550.0000.0000.0990.0940.3860.0000.2470.1530.0720.4750.0660.0310.4920.0740.0530.0180.0510.0360.1150.0000.0000.0000.0000.0000.0000.1440.0430.0490.0000.0630.1520.1520.1080.0750.0000.0000.1510.3320.0930.0770.1850.0000.0000.0000.0380.0000.0000.0060.0190.2720.0000.0990.0000.2210.2540.0800.2130.0000.1020.0000.0000.1500.0000.0730.0000.0000.1260.0000.0000.0120.0000.0460.0000.1850.0000.1450.0000.0000.0690.0000.1500.1880.1510.151
BsmtFinSF2-0.0840.0500.072-0.1180.102-0.112-0.1260.0501.000-0.2710.0700.067-0.1020.002-0.0520.010-0.059-0.154-0.0070.069-0.0690.042-0.0160.0590.068-0.0260.0880.0880.0360.0080.0000.0800.0000.0260.0000.0000.0000.0820.0470.0000.0000.1130.0560.0280.0470.0380.0000.0000.0000.1410.0000.0000.1720.0230.0000.0000.0190.0000.1010.0000.1760.0000.0940.0000.0000.0000.0000.0960.1150.0300.0400.0610.1760.0000.0000.0000.0000.0320.0000.0000.0830.0000.0000.0960.0000.0720.2770.1100.0000.0740.0760.0720.0000.0000.0000.1330.0000.0000.0820.0000.0000.1180.1760.0000.1180.0630.0000.0000.0350.0000.1180.0000.0000.0670.0000.0000.0000.1520.0260.0000.1330.1010.0000.1590.0280.0540.0000.0000.0000.0980.1020.0000.0000.0740.0000.0470.0000.2100.1600.0000.0000.0000.0170.0000.0780.1470.0000.0730.0000.0000.0000.1140.0140.0720.1570.1430.0850.3220.1170.1930.4690.3630.4250.4800.5260.7890.0000.0310.0820.0000.0000.0000.0890.0000.0000.0000.0960.0000.0000.0000.0000.0000.0000.0000.0250.0000.0740.0950.0900.0260.1420.0680.0770.2160.1250.0000.0400.0420.0000.1320.0000.0650.0000.0480.0000.0000.0000.0000.0340.0000.0000.0000.0000.0280.1560.1680.0000.0000.0000.1570.0620.0000.0730.0590.0000.0000.0000.0090.0510.0250.025
BsmtUnfSF-0.1180.0990.0780.273-0.1280.1390.177-0.574-0.2711.0000.3290.2240.0600.0200.2530.1580.2610.1870.109-0.0350.1560.0440.013-0.012-0.0370.0370.2650.0540.1870.1220.0620.0870.1790.0430.0000.0600.0000.1410.1460.0770.0400.0680.0750.0000.0430.0250.0250.0000.3520.0000.0000.0920.0000.1200.0660.0490.1260.0760.1020.0660.1030.0000.0000.0980.2650.1410.0480.1000.0230.1910.0590.0770.0000.1740.0000.1680.0180.1730.1680.0650.2710.0000.0430.1620.2530.1050.0620.1910.0000.2090.0000.0000.0000.0000.0270.0000.0000.0910.1600.0000.1020.0610.0000.0400.2600.1130.0000.0780.0000.0000.0580.0000.0930.1400.0780.0980.0000.0750.0000.0000.2610.1020.0000.0000.1780.2470.2120.3350.0000.2750.3000.0400.0000.0800.0000.0790.1810.2430.2620.2050.0430.0000.2530.0900.1800.1860.0720.0700.0000.1570.1110.1540.0000.2380.2270.1580.2140.0440.1490.6560.0690.0500.0000.1900.0920.3510.0000.0000.0000.0000.0000.0420.2080.0000.0980.0000.1430.0600.0600.0780.0000.0000.0510.0980.2460.0000.2150.2380.0970.0550.0400.0820.0240.0000.1030.1140.1970.0000.0920.0570.1490.1000.1810.1690.0000.1510.0000.0770.1230.0000.0680.0000.0880.0840.0340.0000.0000.0000.0000.0000.2860.0000.2120.0580.0000.0320.0840.1580.2860.0960.096
TotalBsmtSF-0.3190.3540.3660.460-0.2170.4270.2990.4100.0700.3291.0000.829-0.286-0.0810.3710.0590.2340.3200.4870.2310.270-0.1720.0490.0890.0470.0300.2040.0000.2360.0970.0700.3200.2600.0000.0000.0000.0000.2410.2400.1620.0380.0810.0650.0570.0510.0000.0310.0530.1580.0000.1500.1200.0810.1240.0000.1720.0650.0930.1210.0000.0430.0000.0790.1920.3280.1420.0410.0630.0000.0510.1540.1080.0570.1930.0480.1330.1190.1390.1510.0430.3820.0000.0000.2270.0670.1800.0000.2730.0000.2870.0000.0000.0710.0580.0000.1060.0000.1080.0430.0000.1170.0140.0000.1970.1970.1040.0310.0140.0720.0000.1640.0000.1210.0000.0540.1140.0000.0000.0610.1910.1970.1100.0310.0000.1770.3470.3040.4010.0790.3430.4160.0000.1000.0530.0000.0830.1770.1790.3100.4230.0000.0000.4340.0930.2090.3080.0570.0000.0000.2840.1250.2950.0000.2550.0770.0830.3040.0230.0920.0730.0980.0000.0000.0000.0000.1800.0580.0900.0000.0650.0000.1610.2780.1320.0970.0000.1650.2230.2230.1000.1020.0000.0000.1550.4040.1300.2550.3360.0000.0000.0000.0720.0630.0000.0730.0000.3900.0780.0410.0000.3070.2940.1960.3220.0000.0910.0000.0000.1680.0000.0790.0000.0000.1670.0000.0000.0000.0000.0000.0000.3030.0000.2480.0000.0000.0620.0000.1790.3030.1690.169
1stFlrSF-0.2780.3910.4440.409-0.1670.2930.2400.3230.0670.2240.8291.000-0.276-0.0390.4940.1410.3620.2160.4900.2190.235-0.1290.0600.1080.0710.0540.1910.0000.2580.1210.0410.3410.2380.0080.0420.1530.0000.3230.2640.1490.0760.0170.0910.0330.1140.0000.0300.0740.1520.0770.2870.1210.0580.0640.0800.1310.1060.1080.1920.0000.1310.0360.0870.1900.2730.1350.0000.0000.0000.1240.1260.1360.0740.2240.0000.1060.3150.1540.1330.0690.4100.0000.0000.3190.0930.0000.0310.3210.0670.3330.0000.0000.0340.0360.0000.1420.0000.1800.0320.0000.1270.0980.3500.1900.1030.0290.0000.0000.0000.0000.1620.0000.1830.0000.0390.1250.0360.0960.2140.1840.1080.0570.0860.0000.1700.3160.2920.3780.0000.2580.3170.0460.0000.0000.0000.0240.1610.0850.2000.0000.0000.0000.4140.0700.1520.2250.0000.0000.0000.0000.0850.2760.0000.2240.0760.0000.2390.0000.0690.1380.0110.0000.0000.0350.0580.0000.0240.0000.0000.0780.0000.0000.2070.0440.0900.0000.1160.1440.1440.0870.0110.0000.0000.1210.3590.0690.2060.2790.1150.0000.0000.0000.0440.0000.0710.0000.3640.0000.0760.0000.3160.2470.2010.2530.0000.1000.0000.0000.1540.0000.0850.0000.0000.1820.0540.0160.0160.0000.0000.0000.2480.0000.2260.0000.0000.1350.0000.1640.2470.1770.177
2ndFlrSF0.4880.0540.1190.2900.0010.0300.073-0.191-0.1020.060-0.286-0.2761.0000.0580.6430.5100.5870.0790.0980.0710.2250.046-0.0230.0120.0610.0430.1250.0000.4070.4490.0000.1650.2280.0540.0000.1510.0000.2740.2800.0750.0770.0000.0720.0780.1060.0000.0000.0840.0520.0000.2760.2070.0630.1650.0300.0900.2920.1390.1120.0770.2060.0740.0650.3950.1290.1560.1260.1130.0370.0920.1370.0000.0000.1320.0060.0630.2070.1260.6340.0340.8580.2820.1000.8320.1090.0810.0000.1980.1340.2200.0440.0880.1300.0000.0000.0390.0400.1320.0910.0000.1560.0610.0000.0720.2260.1010.0000.0980.0000.0990.0510.0400.1130.0770.2130.1660.0000.1120.0000.0920.2180.0920.0460.0000.1650.2040.0750.2160.0000.2890.3010.0600.0310.0700.0000.0820.2370.2310.2550.0000.0830.0320.2090.0590.2680.2690.0880.0760.0000.0970.1620.1550.0930.2210.1170.0680.1930.0160.0600.2380.0540.0000.0000.0430.0000.1020.0000.0490.0710.1050.0550.0000.2180.0510.1060.0000.1680.0360.0360.0890.0000.0000.0000.0870.1890.0000.2440.2670.0000.0000.0000.0450.0610.0000.0000.0000.2950.0000.4950.0000.2780.2120.1650.2650.1870.0270.0530.0000.0000.0000.0280.0000.0000.0200.0000.0000.0000.0000.0640.0000.0830.0000.0640.0490.0470.0000.0000.0540.0840.2150.215
LowQualFinSF0.076-0.033-0.020-0.0340.040-0.146-0.065-0.0790.0020.020-0.081-0.0390.0581.0000.0640.0210.042-0.023-0.048-0.0420.0100.0480.022-0.0190.066-0.0040.0000.0000.0000.0000.0000.0000.0870.0210.1160.1980.1200.1410.1280.1620.0000.0000.0770.0370.0000.0000.0000.0000.0000.0000.0000.0310.0270.0000.0000.0000.0000.1190.0000.0000.0000.0000.0000.0000.0000.1620.3470.0000.0000.1680.0000.0000.0000.0940.1400.0000.0400.0750.1680.0000.0920.6850.0000.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1170.0000.0000.0000.0510.1040.0000.0000.0000.0000.0000.0000.0000.0000.0000.1200.0000.0000.0000.0000.0490.0870.0000.0000.0460.0810.0000.0000.1750.0000.0000.0000.1950.0560.0000.0730.1380.0000.0650.0000.0000.0000.0000.0570.0920.1020.1960.0000.0000.0800.0000.0000.0000.0450.0000.0000.0000.1680.0160.0820.0310.0000.0000.0000.0000.0000.0000.1600.0000.0000.7030.0000.0290.1280.0600.0000.0000.1260.1260.0370.0000.0000.0000.0000.0000.0790.0800.0640.0880.1830.0000.0600.0000.0000.0600.0000.1350.0310.0000.0000.1260.0000.0760.0380.3240.0290.0000.0000.1450.0000.0570.1250.0000.1680.0000.0000.0000.1250.0000.0000.0000.0000.0000.0620.0000.0000.0000.0000.0000.0190.019
GrLivArea0.2040.3430.4490.603-0.1540.2880.2820.057-0.0520.2530.3710.4940.6430.0641.0000.5430.8280.2760.4680.2270.398-0.0490.0340.0860.0680.0810.1360.0000.4680.3000.0000.3760.2870.0420.0000.0000.0900.1580.1990.1740.0510.0000.1270.0900.0950.0000.0000.0900.0270.0000.1090.1160.0850.1000.0880.1810.1700.1130.1090.0380.2090.0000.0940.3880.1830.0340.1140.1410.0000.0870.0770.0710.0000.0990.0000.0000.0310.1140.0850.1540.3810.3920.0070.4900.1900.0870.0270.1420.0000.1340.0500.0470.0000.0000.0000.0420.0000.1410.1160.0000.1100.0120.0470.2190.2220.0660.0730.0150.0000.0000.0000.0000.1330.1030.2080.1060.0000.0000.0000.2200.2130.0630.0000.0000.1710.2500.1820.2990.1110.3550.4050.0370.0000.1270.0000.1350.0890.2940.3370.0000.0430.0000.3140.0000.2430.3230.0000.0750.0000.0170.0790.1640.0980.1060.1210.1100.1850.0000.0660.1050.0620.0000.0000.0190.0000.0870.0500.0410.0840.0720.0550.0000.2760.0000.0000.0500.2430.1580.1580.0900.0000.0000.0000.1150.3180.0400.2800.3720.0970.0000.0000.0210.0000.0000.0000.0000.2760.0990.3250.0000.2550.2890.1580.2790.4140.0000.0000.0000.2440.1060.0150.0000.0000.2450.0000.0000.0000.0000.0000.0000.1880.0000.1240.0840.0000.0430.0000.0970.1840.2070.207
BedroomAbvGr0.0690.2900.3380.122-0.004-0.035-0.054-0.0840.0100.1580.0590.1410.5100.0210.5431.0000.668-0.0540.1120.0560.1000.002-0.0190.0340.0720.0510.2550.0300.4480.2500.2330.1070.1340.0210.0380.0490.2510.2200.1960.1260.0000.1500.0910.0380.0480.0000.0000.0180.1640.0000.0210.0380.1400.1800.0000.0000.1280.0650.1850.0000.0830.0000.1280.1900.0260.1440.2490.1260.0000.0480.1180.0000.1240.3800.1890.3890.0970.4030.0990.1080.3750.4150.0000.3740.2110.0810.0990.0340.0400.0590.0660.2820.0240.0000.0000.0000.0000.0590.1020.0000.1010.0900.0000.0390.1190.1230.0740.0240.0000.0000.0380.0000.0600.0830.0000.0990.0000.0780.0000.0710.1160.0880.0580.0000.1150.0940.0000.0000.2900.0750.1010.0000.0690.0000.0000.0000.1270.0440.0700.0910.0660.0780.0490.0860.1160.1050.1560.0000.0720.1380.1500.0780.0000.1460.0390.0270.1830.1300.0560.1090.0000.0290.0540.0000.0000.0000.0000.1040.1300.0880.0000.0000.0740.0000.0000.0000.0770.1600.1600.1480.0880.0000.0000.1640.0810.1960.0730.0960.0580.0310.0000.0590.0250.0000.0090.0640.2020.0220.2230.1290.1840.0800.1210.1010.0000.1150.0000.0000.1720.0000.0000.0220.0000.1410.0000.0800.0670.0000.1540.0000.0000.0000.0000.0910.0810.1630.1190.0590.0140.1020.102
TotRmsAbvGrd0.1660.3250.4060.428-0.1050.1770.198-0.050-0.0590.2610.2340.3620.5870.0420.8280.6681.0000.1920.3310.1650.285-0.029-0.0030.0320.0590.0400.0630.0000.3890.2700.1740.2230.2420.0000.0490.0780.2460.2270.2240.1350.0000.0180.0860.0410.0480.0000.0000.0430.0000.0000.0000.1210.0000.1260.0970.0570.1570.0440.3350.0750.1720.0000.1120.2560.1750.0960.2110.1060.0000.0650.0900.0450.0000.2340.1870.1880.1250.2640.0730.1400.3870.4220.1240.4450.2460.0790.0620.1540.0000.1580.0660.2080.0000.0000.0000.0870.0000.1880.0990.0000.1390.0340.0660.0200.2030.0210.0110.0990.0000.0000.0760.0000.1990.0610.0000.1350.0000.0840.0750.0000.2050.0000.0000.0000.1630.2380.1740.2760.2880.2450.2790.0180.0000.0320.0000.0000.0000.2360.2310.0660.0990.0000.2640.0540.1490.2120.1360.0400.0000.0700.1600.0430.0000.1380.1260.0780.0970.0960.0000.1610.0000.0180.0960.0000.0070.0740.0590.0310.0000.0860.0000.0060.2030.0000.0000.0000.1920.1120.1120.1630.0500.0220.0000.1390.2890.1880.2130.2450.0880.0000.0220.0000.0240.0000.0250.0000.2500.0480.3020.0000.2340.2380.1510.2100.1640.0000.0110.0000.1660.0000.0000.0000.0490.1610.0000.0000.0000.0000.0990.0000.1700.0000.1260.1230.0000.0470.0000.1110.1640.1190.119
GarageYrBlt0.0800.0990.0380.594-0.3630.8480.6960.072-0.1540.1870.3200.2160.079-0.0230.276-0.0540.1921.0000.5460.2740.386-0.2970.019-0.101-0.0100.0130.1030.0720.3300.2020.1960.1590.4510.0000.1270.2760.0750.2370.3010.1890.0500.0980.1120.0550.1200.0750.0490.1310.1580.0500.1890.2720.0950.3090.2800.1920.2540.2010.1750.1420.4900.1270.3720.3760.3750.3350.2240.2150.2190.3390.1140.0910.0730.2060.1700.1870.1000.2210.3570.1420.2750.0000.2660.3170.1580.1470.0840.1460.2180.1580.0450.0000.2180.0000.0480.1720.0000.1390.3370.0000.2310.3590.0000.1470.6360.3440.0580.2200.0000.0300.0760.0000.1450.3170.0190.2370.0000.3610.0410.1970.6320.3390.1620.1420.2590.3690.3890.2440.0880.6360.6970.0770.1390.1680.0000.2170.5290.7050.7690.1190.1700.0000.3950.2670.5270.6500.2220.0480.1010.1490.2210.1020.0640.1970.3060.2580.4570.1450.2950.2790.0310.0830.0470.1480.1690.2850.0510.2210.2210.1040.0000.0550.6110.1950.2040.1130.4950.3420.3420.2730.1770.2250.1530.3330.2860.2140.5110.5950.0000.0580.1340.1150.0000.0000.1610.0350.3860.0900.2300.0540.4260.3760.3110.5400.0000.4540.0000.2550.5070.0000.3640.1350.3420.5100.1090.0780.0000.0000.0000.0540.4730.0080.3380.1300.0850.0460.0640.2360.4680.2300.230
GarageArea-0.0470.3520.3670.542-0.2010.5280.3980.244-0.0070.1090.4870.4900.098-0.0480.4680.1120.3310.5461.0000.2480.338-0.1780.0360.0290.0420.0330.1370.0250.2790.1600.0920.2280.7590.0000.1720.1790.0900.1930.2790.1270.0340.1360.0960.0000.0990.0040.0000.0860.0180.0000.1750.1770.0000.1510.1540.2200.1820.1610.1340.0480.1410.0760.2190.2360.3630.1610.1550.0800.0590.2110.1420.1100.0550.1630.1720.1400.0660.1580.2600.1000.0520.0320.0000.1530.0880.0840.0510.1280.0640.1550.0000.0280.1020.0740.0000.0840.0000.1310.0970.0510.1380.1680.0000.1630.2750.1690.0410.0880.0000.0000.0490.0000.1330.1000.0860.1430.0510.1420.0000.1490.2770.1610.0710.0000.2050.3530.3320.3710.2340.3780.4380.0540.1940.0730.0740.1370.2690.2420.3850.0700.0000.0000.4430.0610.3370.3850.1230.0610.0190.0850.1650.1890.1150.2410.1050.1300.3220.0550.1070.0830.0000.0000.0000.0330.0000.0280.0000.1060.0000.1580.0190.0170.2860.1220.0930.0000.1710.2760.2760.2040.1300.0000.0000.2530.3810.2580.3230.3810.0430.0000.0000.0000.0000.0000.0310.2820.3780.0180.1340.0000.3480.2730.2710.4420.0440.2520.0000.0000.7300.0540.1730.0050.0380.7660.0000.0000.0000.1880.0000.0000.3220.1010.2440.0900.1430.0000.0000.1820.3200.2150.215
WoodDeckSF0.0230.1080.1840.259-0.0430.2880.2300.1790.069-0.0350.2310.2190.071-0.0420.2270.0560.1650.2740.2481.0000.124-0.158-0.028-0.0900.0500.0380.1900.0000.2480.0820.0000.1460.1480.0310.0000.0000.0000.1760.1450.0600.0730.1730.0880.0830.0820.0000.0000.0830.1900.0000.0000.0890.1360.1400.0000.0670.1490.0860.1850.0000.1540.0000.0670.1520.1780.1100.0270.0000.0960.0000.0940.1050.2950.1020.1000.0640.0000.1610.1090.0360.0070.0690.0000.0910.0000.0500.1030.0850.0000.0770.0430.0180.0480.0000.0000.0390.0000.1130.0430.0000.1170.1080.0000.0000.2400.1160.1860.0490.0000.0000.0000.0000.1210.0410.0000.1280.0000.1150.0000.0000.2410.1300.1490.0310.1370.1980.1620.1000.0000.2800.2990.0000.0500.0730.0730.0670.1550.2110.3160.0000.0000.0000.1930.0540.2510.2860.0000.0680.0000.0000.2600.1850.0000.2550.0550.1000.2520.0000.1350.0560.0730.0730.0000.1210.0000.0620.0000.0530.0000.0000.0000.0000.2390.0820.1110.0000.1630.1520.1520.1450.0000.0000.0000.1650.1370.0710.2650.2790.1820.0000.0740.0780.0000.0000.1450.0680.2290.0000.1360.0000.2550.2490.1140.2630.0000.0000.1440.0000.1370.0000.0790.1310.0000.1440.0690.0000.0000.0000.0000.0000.0930.0000.0180.0000.0000.0850.0000.0340.0850.1790.179
OpenPorchSF0.0320.1630.1770.435-0.1330.3930.3530.081-0.0690.1560.2700.2350.2250.0100.3980.1000.2850.3860.3380.1241.000-0.1690.0170.0070.0370.0660.0790.0000.1660.1490.0000.1220.1290.0000.1710.2070.0000.1520.1280.0720.0000.0000.0280.0480.0320.0000.0000.0700.0000.0000.0000.0650.0240.0000.0000.0620.0000.0750.0000.0120.1050.0000.0630.1750.1290.1170.0180.0820.0000.2220.0000.0000.0000.0700.0000.0640.0000.0000.1070.0000.0640.0890.2760.1910.0000.0000.0840.0580.0000.0710.0000.0000.0000.0000.0000.0710.0000.1250.0000.0440.0700.0420.0000.0110.2050.1400.0000.0000.0000.0000.0000.0000.1300.0000.0000.0620.0000.0310.0000.0500.2140.1740.0480.0000.1310.1740.1140.2050.0000.2200.2620.3340.0000.0000.0000.0680.1330.1770.2600.0200.0990.0000.2010.0000.1470.2010.0150.0000.0000.0000.0000.0490.0640.0490.0900.0000.1790.0600.0580.0000.0000.0000.0340.0490.0730.0460.0000.1630.2160.0000.0000.0000.1930.1000.0350.0000.1650.1010.1010.1020.0000.0000.0000.1070.1700.0000.1930.2440.0000.0000.0000.0000.1400.1840.0650.0210.1880.0000.0950.0000.1790.1340.1520.2230.0850.0740.1440.0000.1410.0000.0000.0000.0000.0690.0000.0000.0000.0000.1120.0000.1870.0000.1220.0400.0000.0000.0000.1180.1800.0830.083
EnclosedPorch0.011-0.099-0.067-0.1620.110-0.409-0.235-0.1480.0420.044-0.172-0.1290.0460.048-0.0490.002-0.029-0.297-0.178-0.158-0.1691.000-0.039-0.0810.004-0.0290.0300.0490.1070.0780.0360.0430.2470.0240.1360.0450.1190.1950.2270.0940.0000.0000.0000.1100.0450.0000.0430.0410.0000.0000.0000.0750.1080.1090.0660.0000.0600.2140.0000.0000.0380.0000.0000.0080.0580.2770.1490.0000.0000.0660.0000.0000.0000.0710.0460.0000.0000.0560.1890.1030.0770.0000.1840.0390.0000.0000.0000.0710.0810.0670.3720.0400.1080.0000.0000.0450.1290.0000.0500.1290.1410.0000.2600.3170.1760.1580.0000.1100.0000.0000.0700.1290.0000.0230.1170.1330.0000.0320.1510.3500.1800.1190.1400.0000.1320.1870.0700.0240.0000.1440.1570.0000.0340.0920.0000.1140.3590.0300.1850.0400.4170.0000.0840.1590.1910.2060.2030.0000.0000.0580.0830.0430.0000.1010.0000.0000.1540.1020.0000.1600.0250.0000.0730.1360.0000.0950.0000.1820.2400.0410.1050.0000.1530.1070.1170.1670.0600.2290.2290.1130.1040.0000.0000.1590.0510.0810.1260.1230.0090.1410.0000.0000.0000.0000.0150.0000.1860.0000.0520.0000.1950.1360.0860.1650.0000.1920.0000.1140.1800.0000.2020.0000.1360.1780.0460.0000.0000.0000.0000.1390.0840.0000.0250.1460.0000.0000.0000.0720.0860.1320.132
3SsnPorch-0.0360.0500.0620.0330.0320.0220.0520.047-0.0160.0130.0490.060-0.0230.0220.034-0.019-0.0030.0190.036-0.0280.017-0.0391.000-0.038-0.0090.0370.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1080.0800.0640.0000.0980.0750.0000.0370.0000.0000.0000.0000.0500.0000.0710.0000.1020.0000.0000.1220.0610.0000.0570.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0790.0000.0050.0000.0000.0000.0000.1120.1130.0460.0000.0630.0000.0000.0000.0000.0000.1650.0000.0000.0740.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1850.0000.0000.0610.0000.0000.0000.0790.0000.0000.0000.0000.0000.0000.0820.0670.0000.0670.0000.0590.0560.0000.0000.0450.0000.0270.0000.0000.0000.0000.0000.5720.0000.0290.0000.0000.0000.0000.0000.0000.0000.0530.0000.0000.0000.0910.0000.0000.0000.0370.0000.0000.0000.0000.0580.0000.0000.0000.0000.0000.0000.0000.0570.0000.0570.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0630.0510.0000.0000.0000.0320.0000.0000.0000.1090.0000.0000.0000.0000.0000.0000.0860.0620.0000.0000.1710.0000.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
ScreenPorch-0.0220.0400.0920.0460.075-0.073-0.0460.0720.059-0.0120.0890.1080.012-0.0190.0860.0340.032-0.1010.029-0.0900.007-0.081-0.0381.0000.0190.0240.0000.0000.0460.0460.0000.1240.0000.0000.0000.0000.0000.0520.0280.0000.0000.0780.0000.0000.1220.0000.0000.0000.0000.0000.0000.1000.0550.0000.0790.0000.0400.0000.0000.0000.0350.0140.0670.1380.0000.0500.0000.0000.0300.0000.0740.0000.1100.0000.0000.0000.0000.0390.0680.0000.0000.1870.0880.0290.0000.0000.1320.1210.0000.0740.0620.0000.0000.0000.0000.1480.0000.0430.0000.0000.0000.1060.0000.0000.1070.1280.0000.0000.0000.0000.1500.0000.0000.0000.0000.0000.1920.1000.0000.0000.1120.1340.0000.0000.0000.0000.0320.0310.0000.0720.0480.0000.0000.0000.0000.0000.0840.0810.0640.0000.0000.0000.0000.0000.0340.0470.0000.0000.0000.0000.0300.0460.0400.0000.0810.0000.0000.0430.1000.0250.0000.0000.0000.1160.0820.0340.0000.1140.1690.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0000.0000.0770.0000.0150.0220.0000.0000.0000.0000.1910.0000.0500.0750.0000.0630.0000.0000.0250.0150.0350.0170.3230.1040.0470.0000.0720.0000.0000.0830.0000.0430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0430.0000.0000.0000.0000.0000.0280.028
PoolArea0.0330.0780.0840.057-0.0060.0090.0030.0580.068-0.0370.0470.0710.0610.0660.0680.0720.059-0.0100.0420.0500.0370.004-0.0090.0191.000-0.0230.0990.0000.1060.0000.0000.1860.0000.0000.0000.0000.0000.0000.0000.0660.0000.0000.0040.0640.0870.0000.0000.0540.0000.0000.0000.0000.0000.0000.0000.1040.0000.0000.0000.0550.0000.0000.1180.0660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.0480.0000.1270.2710.0550.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0430.0000.0000.1290.0000.2760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0460.1720.0000.0000.1070.0000.2700.0000.0000.0000.0000.0000.0000.0220.0770.0000.0000.0330.0000.0000.0790.0000.0660.0000.0230.0130.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0760.0190.0000.0000.0450.0000.0350.0000.0000.0250.0000.0000.0000.1860.0000.0610.0000.0000.0000.0000.0000.0000.0000.1310.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0140.0000.0000.2610.0000.0610.0000.0000.3280.0000.0670.0000.0000.0000.0000.0000.0000.0260.0000.0000.0600.0000.2830.0000.0850.0240.0000.000
MoSold0.0180.0220.0060.061-0.0070.0190.021-0.016-0.0260.0370.0300.0540.043-0.0040.0810.0510.0400.0130.0330.0380.066-0.0290.0370.024-0.0231.0000.0000.0000.0440.0550.0200.0000.0000.1550.0000.0000.0190.0480.0650.0490.1050.0700.1020.0760.0000.0000.0000.0770.0700.0000.0000.0570.0570.0920.0740.0800.0000.0000.0000.0000.0530.0510.0420.0190.0580.0000.0430.0000.0000.0000.1000.0000.0000.0170.0270.0000.0000.0630.0250.0000.0000.0000.0000.0160.0000.0510.0500.0000.0310.0000.0000.0110.0000.0000.0000.0520.0660.0000.0000.0000.0160.0110.0000.0000.0080.0000.1110.0280.0000.0460.0000.0660.0000.0000.0290.0000.0370.0260.0470.0000.0260.0000.0640.0000.0000.0360.0410.0000.0620.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0550.0000.0000.0000.0000.0190.0280.0000.0000.0220.0490.0000.0400.0630.0000.0000.0230.0000.0000.0000.0190.0440.0430.0000.0000.0000.0000.0000.0000.0000.0000.0520.0540.0200.0000.0000.0000.0200.0000.0000.0200.0000.0000.0410.0000.0710.0340.0000.0000.0790.0510.0370.0000.0000.0000.0000.0410.0520.0300.0000.0210.0320.0000.0000.0000.0000.0000.0510.0550.0000.0000.0000.0000.0000.0000.0000.0090.0260.1030.0350.1120.0650.0000.0660.0000.1130.0930.0370.037
BsmtFullBath0.2090.1370.2110.0660.0000.1440.1220.3970.0880.2650.2040.1910.1250.0000.1360.2550.0630.1030.1370.1900.0790.0300.0000.0000.0990.0001.0000.0970.2640.1520.1290.1120.1150.0520.0270.0000.0000.1400.1180.0750.0000.1690.1140.0000.0370.0000.0000.0000.0470.0000.0420.0680.0800.0530.0000.1010.0650.0790.0000.0840.0440.0170.0000.0860.0620.1490.0000.0000.0300.0300.0850.0760.1460.1260.0000.2780.0000.0360.1340.0300.1430.0000.0570.1130.2040.0520.0540.0960.0000.1140.0370.1750.0000.0000.0000.0540.0000.0590.0330.0000.0000.1540.0000.0190.0570.0360.0000.0240.0000.0000.0810.0000.0460.0320.0000.0000.0000.1280.0000.0000.0540.0510.0000.0000.0780.1420.1100.0650.0300.0920.1080.0000.0140.0000.0000.0000.1390.0410.0920.0990.0290.0000.1450.0630.0740.0910.0730.1170.0000.1160.1410.2990.0300.2370.1380.0470.4510.0250.0160.4950.0640.0570.0790.0520.0220.0670.0000.0540.0330.0000.0000.0000.1100.0350.0710.0000.0380.1060.1060.0920.0450.0000.0000.1220.1170.0650.0780.1140.0000.0000.0000.0000.0000.0000.0000.0000.1960.0000.0520.0650.1450.1130.0740.1330.0000.0780.0000.0000.1400.0000.0510.0000.0000.1170.0000.0000.1750.0000.0000.0000.0000.1400.0000.0000.0000.3590.0580.0980.0000.0670.067
BsmtHalfBath0.0860.0000.0000.0620.1020.0890.0770.0280.0880.0540.0000.0000.0000.0000.0000.0300.0000.0720.0250.0000.0000.0490.0580.0000.0000.0000.0971.0000.1640.1540.4980.0000.0720.0240.0000.0370.0000.0580.0250.0360.0090.0140.0000.0000.0650.0190.0000.0000.1650.0000.0000.0000.0670.0380.0000.0520.0000.0130.0000.0000.0820.0000.1040.0000.0440.0000.0000.0000.0000.0490.0000.0000.0000.0820.0000.0860.0000.0470.0000.0000.0000.0000.0000.0080.1070.1180.1900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0070.0310.0000.0000.0380.0000.1300.0000.1160.0000.0090.0000.0000.0460.0000.0220.0000.0410.0000.0000.0330.0000.0150.0540.0560.0370.0000.0000.0000.0730.0750.0410.0000.0830.0000.0680.0000.1180.0860.0000.0000.0000.0510.0000.0400.0790.0000.0980.0000.0430.0000.0700.0000.0480.0770.0340.0000.0400.0000.1040.0690.0000.0000.0480.1060.0970.0000.0000.0000.0000.0000.0000.0520.0000.0630.0000.0000.0160.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0630.0000.0000.0000.0000.0690.0000.0000.0000.0000.0670.0000.0260.0000.0000.0000.0060.0740.0000.0430.0200.3510.0000.0330.0710.0740.0010.001
FullBath0.2490.1210.0980.4040.3090.3510.2700.1580.0360.1870.2360.2580.4070.0000.4680.4480.3890.3300.2790.2480.1660.1070.0000.0460.1060.0440.2640.1641.0000.2300.1130.1800.3290.0000.1110.1770.0000.1270.2360.1150.0280.1520.0990.0350.0230.0590.0200.0000.1120.0000.0640.1790.0540.1390.0000.1210.2100.1480.0840.0180.2910.0590.1220.1610.2000.1210.0000.1500.0790.2060.0920.1120.0840.1340.0000.1680.0000.0720.1310.1000.1890.0330.0000.3590.1810.0370.0000.0800.0000.0970.0000.2300.0810.0000.0000.0000.0000.0850.1100.0000.1690.1250.0000.0340.3460.1950.0480.0810.0000.0000.0120.0000.0700.0700.0000.1660.0000.0820.0540.0410.3350.1980.0300.0000.1710.2810.2070.2240.1000.4750.4980.0830.0850.0560.0000.0940.1590.4000.4920.0000.0000.0000.2630.0710.4540.5440.0670.0720.2300.0240.1030.1190.0000.1480.1230.2020.2960.0600.1770.0870.0000.0650.0000.0000.0530.0930.0000.0000.0000.0630.0000.0200.3430.0920.0870.0000.2590.1030.1030.1980.0570.0000.0000.2030.1870.1010.3990.4330.1140.0470.0270.0000.0000.0000.0000.0460.2860.0000.2870.0960.3210.2990.2210.4060.0000.1130.0000.0000.1560.0000.1030.0000.0000.1690.0390.0000.0000.0440.0000.0000.2440.0000.1600.0900.0000.2040.0000.1440.2370.1700.170
HalfBath0.5120.0000.0000.2250.0790.2270.2000.0120.0080.1220.0970.1210.4490.0000.3000.2500.2700.2020.1600.0820.1490.0780.0000.0460.0000.0550.1520.1540.2301.0000.1920.1640.1970.0000.0290.1560.0570.0540.1150.0120.0000.0110.0490.0000.0460.0000.0000.0290.0920.0320.1330.1000.0000.0000.0000.1040.2170.0610.0000.0970.1120.0000.0950.1860.0000.1130.0000.0640.0250.1060.0000.0000.0540.1380.0870.2960.1050.0000.0400.0670.4890.0000.0000.6370.1380.0270.0230.0360.0000.0550.2150.2000.0410.0000.0000.0000.0000.0000.0780.0000.0710.0830.0000.0000.1020.1110.0170.0410.1610.0340.0000.0000.0000.0880.0000.0730.0000.0760.0000.0000.1090.0900.0480.0000.1440.1080.0460.0680.0290.1910.2060.0000.0560.0630.0000.0890.1070.1350.2140.0930.0000.0000.0600.0550.2000.1790.0720.0000.0000.0920.0970.0000.0000.1060.0000.0300.0540.0000.0430.0560.0000.0360.0000.0000.0510.0990.0000.0320.0000.0390.0000.0170.1350.0350.0000.0000.1200.1300.1300.1110.0390.0000.0000.1270.0980.0690.1540.1790.0000.0260.0540.0080.0000.0000.0610.0000.0870.0520.2540.0840.1430.1970.0900.1920.0000.0340.0000.0000.1430.0000.0430.0000.0390.1590.0240.0000.0320.0480.0000.0000.0510.0000.0000.0450.0000.1610.0730.0230.0430.1250.125
KitchenAbvGr0.4760.0110.0000.1060.0730.2140.1140.0000.0000.0620.0700.0410.0000.0000.0000.2330.1740.1960.0920.0000.0000.0360.0000.0000.0000.0200.1290.4980.1130.1921.0000.0860.1230.0000.0000.0150.1290.0880.0970.0270.0000.0000.0000.0000.0990.0000.0000.0110.0000.0000.0000.0000.0000.0590.0000.0880.0270.0000.0000.0770.0200.0000.0000.0000.0260.1920.0000.0000.0000.0320.0000.0000.0000.4460.3570.7590.0000.0460.0000.0000.0290.0000.2150.0000.1640.0150.2730.0410.0000.0220.0000.0680.1730.1130.0000.0000.0000.0090.0000.0000.0000.0800.0000.0000.0700.0000.1910.1610.0450.0000.0000.0000.0060.0000.0000.0000.0000.1090.1510.0000.0810.0000.0000.0000.0290.0500.0060.0000.0670.1360.1320.0000.0490.0000.0000.0000.0350.0580.1480.2550.0780.0000.0490.0280.1310.0560.0000.1170.0000.1710.0350.0770.0000.0230.0350.0000.0790.0000.0000.0480.0000.0000.0000.0000.0000.0960.0000.0680.0000.0000.0000.1740.1670.0540.0000.0000.1380.2450.2450.0810.1090.1290.0000.1390.0390.0500.1550.1620.0000.0000.0000.0000.0620.0000.0160.1340.1780.0000.0000.1460.0800.1000.1030.1150.0000.0930.0130.0450.2030.0000.0280.0000.2640.2030.0000.0000.0000.0000.0000.0000.0730.0450.0170.0000.5010.2340.0400.0650.0730.0880.088
Fireplaces0.1930.2120.1600.2670.1050.1690.1360.2980.0800.0870.3200.3410.1650.0000.3760.1070.2230.1590.2280.1460.1220.0430.0000.1240.1860.0000.1120.0000.1800.1640.0861.0000.2020.0300.0550.0650.0900.2340.1840.0260.0630.0910.0800.0430.0800.0300.0000.0490.0980.0000.0870.0410.1600.1070.2070.1380.1700.0990.0000.0650.1090.0290.1370.1480.1810.1350.0290.0400.0000.0600.0950.1010.0680.1250.0490.1710.0870.0810.0410.0000.0790.0140.0000.1400.1010.0430.0710.1390.0000.1300.0000.0000.0630.0000.0000.1380.0000.0000.0000.0000.1280.1270.0000.0730.1270.0510.0000.0480.0000.0000.0770.0000.0000.0000.0000.1210.0000.1070.0000.0770.1270.0680.0000.0000.1590.2240.1250.1710.0610.2480.2820.0000.0630.0460.0000.0630.0760.1460.2090.0410.0000.0160.2080.0830.1760.2240.0300.0310.0000.0360.0190.2180.0470.1180.0000.0920.1190.0430.0820.1320.0370.0560.0000.0000.0470.0440.0000.0400.0130.0000.0000.0320.1720.0140.0470.0000.1350.1960.1960.1260.0740.0160.0000.1630.2260.0820.1850.2310.0000.0300.0080.0000.0000.0000.0470.0000.2760.0920.1710.0290.2810.2990.1150.2660.0000.0560.0710.0160.1690.0000.0760.0000.0000.2140.0760.0000.0410.0000.0000.0000.1190.0160.0780.0890.0320.0640.0000.0000.1100.2080.208
GarageCars0.2430.1790.0110.4020.2190.3400.2760.1760.0000.1790.2600.2380.2280.0870.2870.1340.2420.4510.7590.1480.1290.2470.0000.0000.0000.0000.1150.0720.3290.1970.1230.2021.0000.0000.0370.1410.0930.1770.2370.1000.0940.0790.0400.0000.0820.0370.0000.0930.0780.0000.1110.1580.0450.1390.0790.2270.1760.1760.1330.1270.2300.0450.1880.2630.3870.1370.1360.1170.0740.1740.1160.1420.0360.1930.1660.2000.0620.1450.2470.0690.0000.0520.0000.2130.1030.0790.0290.1750.0630.2080.1620.0000.1110.0950.0000.0610.0000.1250.1210.0460.2040.1620.0000.0830.3630.1950.0460.0950.0250.0000.0060.0000.1280.1170.0530.2050.0460.1320.0000.0790.3620.1990.0490.0000.2190.3850.3440.3520.1850.4470.5240.0310.1960.1310.0950.1760.2230.3590.4940.0630.1850.0000.4930.0640.4200.5170.1470.0690.0590.0980.1500.1940.0270.2380.1240.2040.3490.1090.1550.0870.0000.0330.0040.0660.0630.1010.0000.1110.0880.1560.0520.0000.3510.1090.0980.0000.2690.2830.2830.2200.1140.0250.0000.2600.4060.2550.3690.4400.0670.0380.0540.0400.0000.0000.0760.2040.3610.0340.1790.0000.3370.3480.2410.4760.0260.1780.0310.0580.7270.0360.1160.0000.0810.7670.0410.0310.0000.0380.0000.0000.3650.1120.2680.1060.1520.1300.0310.2200.3630.2100.210
YrSold0.0000.0090.0000.0000.0500.0000.0000.0000.0260.0430.0000.0080.0540.0210.0420.0210.0000.0000.0000.0310.0000.0240.0000.0000.0000.1550.0520.0240.0000.0000.0000.0300.0001.0000.0000.0000.0000.0000.0000.0000.0390.0000.0360.0630.0000.0000.0320.0710.0320.0000.0000.0390.0000.0000.0500.0000.0480.0000.0000.0000.0000.0250.0000.0000.0000.0000.0280.0580.0480.0000.0000.0000.0440.0000.0000.0000.0000.0000.0000.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0160.0100.0480.0430.0000.0000.0000.0000.0000.0740.0280.0000.0400.0000.0330.0000.0000.0340.0660.0000.0000.0050.0200.0000.0740.0000.0000.0000.0000.0190.0120.0000.0000.0000.0000.0600.0320.0630.0000.0000.0240.0000.0000.0480.0000.0350.0000.0000.0690.0050.0000.0330.0000.0000.0000.0000.0970.0000.0750.0540.0300.0000.0410.0000.0000.0480.0000.0000.0260.0000.0000.0180.0140.0000.0000.0000.0000.0180.0000.0500.0450.0080.0000.0320.0000.0000.0000.0000.0000.0000.0260.0000.0000.0280.0000.0000.0000.0390.0000.0000.0640.0830.0000.0370.0500.0000.0280.0000.0000.0000.0000.0000.0000.0000.0350.0540.0180.0330.0000.0000.0340.0000.0000.0580.0470.0200.0550.0310.0220.1380.0180.0640.0690.0000.0000.0610.1390.1300.0000.000
MSZoning_C (all)0.0620.0000.0000.2510.1740.1520.1340.0000.0000.0000.0000.0420.0000.1160.0000.0380.0490.1270.1720.0000.1710.1360.0000.0000.0000.0000.0270.0000.1110.0290.0000.0550.0370.0001.0000.0000.0000.1480.0000.0000.0000.0620.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0610.0000.0000.0000.0310.0100.0000.0000.0000.0040.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0660.0000.0000.0000.0000.0440.0000.0000.0000.0380.0550.0000.0000.2030.0430.0000.0000.0750.0000.0000.0190.0000.0250.0600.0000.0540.0000.0000.0000.0570.0800.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0380.0380.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0530.0000.0000.0000.0600.1610.1610.0930.0000.0000.0000.0730.0000.0580.0530.0320.0000.0000.0000.0000.0000.0000.0000.0000.0890.0070.0000.0380.0470.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.0530.0000.0000.1500.0000.0000.0000.0000.0470.1220.0000.0280.0000.0760.0000.0270.027
MSZoning_FV0.3310.1000.0000.2100.1610.3130.2650.0190.0000.0600.0000.1530.1510.1980.0000.0490.0780.2760.1790.0000.2070.0450.0000.0000.0000.0000.0000.0370.1770.1560.0150.0650.1410.0000.0001.0000.0000.4120.0820.0270.0180.0000.0620.0250.0250.0000.0000.0330.0000.0000.0000.0240.0000.0620.0180.0450.0360.0000.0000.0170.0840.0000.0330.0050.0350.0500.0000.0330.0250.8560.0000.0000.0000.1390.0000.0190.1270.1520.0640.0000.0880.0000.0000.1910.0000.0280.0000.0730.0000.0640.0000.0000.0000.0000.0000.0180.0000.0750.0830.0000.1150.0480.0000.0000.0690.0780.0000.0000.0000.0000.0000.0000.0760.0790.0000.1190.0000.0600.0000.0000.0730.0760.0000.0000.0370.0000.0500.0000.0000.2360.2300.0000.0000.0610.0000.0710.0610.1840.2370.0000.0000.0000.0000.0000.1930.1880.0120.0000.0000.0000.0000.0570.0000.0420.0620.0370.0990.0330.0440.0470.0000.0000.0000.0130.0210.0780.0000.0000.0000.0000.0000.0000.1670.0170.0270.0000.1330.0430.0430.0420.0000.0000.0000.0540.0000.0000.2040.1860.0000.0000.0000.0000.0000.0000.0450.0000.0000.0000.0000.0000.0190.0140.0740.0730.0000.0160.0000.0000.0620.0000.0000.0000.0000.0570.0090.0000.0260.0000.0000.0000.1910.0000.1440.0000.0000.0000.0000.1170.1870.0660.066
MSZoning_RH0.0960.0000.0000.0100.0490.0730.0650.0000.0000.0000.0000.0000.0000.1200.0900.2510.2460.0750.0900.0000.0000.1190.0000.0000.0000.0190.0000.0000.0000.0570.1290.0900.0930.0000.0000.0001.0000.1930.0230.0870.0220.0000.0800.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2130.0000.1260.0000.0000.0000.0000.0640.0460.0630.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0120.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0530.0000.0000.0000.0000.0100.0000.0000.0000.0000.0310.0000.0000.0870.0470.0230.0000.0000.0000.0000.0000.0580.0000.0220.0000.0000.0000.0000.0000.0350.0360.0720.0000.0000.0310.0000.0000.0000.0000.0230.0310.0150.0000.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0310.1160.1160.0610.0000.0000.0000.0680.0000.0350.0460.0370.0000.0000.0000.0000.0000.0000.0000.0360.0770.0000.0000.0210.0210.0000.0540.0280.0000.0000.0000.0000.0570.0000.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0840.0000.0000.0000.0100.0000.0190.019
MSZoning_RL0.4310.3680.0350.1440.1420.4240.2440.2020.0820.1410.2410.3230.2740.1410.1580.2200.2270.2370.1930.1760.1520.1950.0000.0520.0000.0480.1400.0580.1270.0540.0880.2340.1770.0000.1480.4120.1931.0000.8060.0530.0000.0490.0000.0060.1230.0000.0460.0890.0200.0410.1930.1460.0610.1140.0410.0660.1170.3050.2000.0370.2080.0150.1120.0790.1080.4490.0000.0970.0530.3290.0560.0750.0240.2830.0890.0000.2310.2010.1450.0740.1930.0320.1170.0880.0000.0790.0300.1110.0330.1160.0000.0000.1020.0000.0000.0270.0000.1540.1350.0000.1570.1280.0000.0110.0710.0770.0000.1160.0000.0000.0000.0000.1570.1290.0000.1600.0000.1540.0000.0430.0700.0680.0000.0090.1350.1540.0290.0420.1090.0580.0520.0180.0630.0170.0000.0600.2870.1320.0330.0000.0520.0000.0620.0960.0820.0980.1030.0340.0000.0000.0720.0880.0000.1310.0900.0270.0770.0260.0070.1910.0000.0430.0040.0540.0560.1020.0000.0720.0660.0000.0410.0000.0590.0280.0340.0000.0000.1980.1980.1170.0230.0180.0000.1340.0730.0590.0500.0690.0350.0000.0000.0000.0000.0000.0000.0000.3020.0000.0740.0000.3200.1110.1520.1940.0180.1420.0000.0180.1750.0000.0960.0000.0410.1440.0000.0000.0000.0220.0000.0000.0000.0000.0000.0540.0000.0000.0000.0350.0000.2660.266
MSZoning_RM0.3770.3540.0000.2040.2220.4690.2690.1820.0470.1460.2400.2640.2800.1280.1990.1960.2240.3010.2790.1450.1280.2270.0000.0280.0000.0650.1180.0250.2360.1150.0970.1840.2370.0000.0000.0820.0230.8061.0000.0510.0000.0540.0000.0000.1030.0000.0200.0610.0000.0570.2410.2030.0440.0710.0340.0410.0920.2680.2490.0000.1740.0000.0880.0600.0820.5580.0400.0700.0770.0970.0400.0570.0000.2200.0880.0000.1920.1370.1950.0830.1550.0220.1280.0000.0250.0520.0140.0800.0000.0840.0000.0000.1210.0000.0000.0000.0000.1360.0850.0000.0690.1120.0000.0490.1070.1280.0000.1210.0000.0000.0000.0000.1380.0810.0000.0690.0000.1320.0000.0620.1050.1210.0220.0000.1050.1480.0600.0480.0400.1790.1850.0360.0550.0770.0000.1080.3430.0440.1570.0000.0400.0000.0760.1260.1800.1900.0940.0410.0000.0000.0790.0430.0000.1150.0360.0130.1290.0690.0130.1760.0000.0170.0400.0400.0250.0550.0000.0980.0800.0310.0570.0000.1440.0720.0650.0000.0600.1800.1800.1180.0420.0360.0000.1460.0670.0490.1510.1660.0620.0000.0000.0000.0000.0000.0600.0000.3020.0000.0650.0000.3200.1280.2040.2530.0360.1850.0000.0360.2160.0000.1140.0000.0630.1680.0000.0000.0000.0000.0000.0000.0990.0000.0710.0000.0000.0000.0320.0400.0940.2390.239
LandContour_Bnk0.1420.1520.1070.1020.0820.2430.1830.1940.0000.0770.1620.1490.0750.1620.1740.1260.1350.1890.1270.0600.0720.0940.0000.0000.0660.0490.0750.0360.1150.0120.0270.0260.1000.0000.0000.0270.0870.0530.0511.0000.0160.0000.6240.0000.0410.0130.0000.0380.0000.0000.0000.0220.0000.0490.0940.0130.0000.0800.0000.0000.0520.0000.0320.0000.0340.0790.2430.0000.0000.0380.0000.0000.0000.0000.0940.0000.0070.0490.0940.0170.0460.1420.0750.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.0610.0000.0000.0260.1220.1490.0000.0000.0000.0000.0000.0000.0000.0460.0000.0740.0000.0000.0000.0550.1190.1560.0000.0110.1040.1080.0000.0000.1320.1230.0890.0000.0000.0000.0000.0000.1230.0000.0870.0000.0000.0090.0000.0600.1070.0950.0420.0000.0000.0000.0110.0050.0000.0280.0100.0000.0900.0440.0990.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0730.0000.0000.0000.0850.1260.1260.0550.0520.0090.0000.0930.0000.1400.0840.0290.0170.0000.0070.0000.0000.0000.0130.0000.1490.0750.0190.0400.1100.0630.1140.1300.0000.0380.0000.0000.0740.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0000.0210.0000.0100.0110.0000.1460.0000.0000.0230.0000.000
LandContour_HLS0.0490.1010.1030.1300.0000.0860.1080.0460.0000.0400.0380.0760.0770.0000.0510.0000.0000.0500.0340.0730.0000.0000.1080.0000.0000.1050.0000.0090.0280.0000.0000.0630.0940.0390.0000.0180.0220.0000.0000.0161.0000.0000.5520.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0370.1980.0120.0750.0000.0000.0000.0350.0000.0230.0000.0000.0000.0000.0230.0130.0290.1320.1920.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0320.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0480.0490.0000.0000.0340.0000.0000.0000.0000.0000.0000.0570.0740.0000.0000.0000.0830.0000.0200.0610.0000.0000.0000.0190.0000.1660.0000.1090.0180.0180.0340.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0180.0500.0000.0400.0000.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0820.0000.0340.0130.0000.0000.0030.0480.0800.1400.140
LandContour_Low0.0000.0470.4130.2270.1580.1010.0720.1350.1130.0680.0810.0170.0000.0000.0000.1500.0180.0980.1360.1730.0000.0000.0800.0780.0000.0700.1690.0140.1520.0110.0000.0910.0790.0000.0620.0000.0000.0490.0540.0000.0001.0000.4640.0220.0900.0000.0000.0000.0000.0000.0000.0000.3800.0000.0000.0000.0110.0000.0000.0760.0560.0000.0030.0000.0090.0270.0000.0050.0380.0150.0150.0000.0000.0120.0000.0000.0000.0280.0000.0000.0420.0000.0000.0340.0000.0000.1950.0100.0000.0110.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0950.0000.0000.0710.0000.0000.0000.0000.0000.0150.0000.0000.0150.0000.0000.0000.0700.0000.0000.0690.0000.0000.0420.0460.0270.0000.0000.0940.0000.0000.0000.0000.0000.0000.0000.0160.0650.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0050.0000.2620.0280.1650.0000.0000.0170.0000.0000.0640.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0160.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0140.0000.0000.0000.0000.0000.0000.0910.091
LandContour_Lvl0.0890.1480.3040.1050.0840.1600.1370.1220.0560.0750.0650.0910.0720.0770.1270.0910.0860.1120.0960.0880.0280.0000.0640.0000.0040.1020.1140.0000.0990.0490.0000.0800.0400.0360.0630.0620.0800.0000.0000.6240.5520.4641.0000.0000.0250.0130.0000.0000.0000.0000.0000.0000.2240.0680.1870.0000.0000.0630.0000.0170.1000.0000.0670.0260.0590.0000.1360.0570.0000.0750.0820.1050.0000.0000.0630.0060.0450.0450.0430.0000.0000.0780.0250.0290.0200.0000.0970.0190.0000.0000.0000.0000.0150.0000.0000.0140.0000.0000.0000.0000.0440.0000.0000.0440.1060.0840.0000.0150.0000.0000.0000.0000.0000.0000.0000.0490.0000.0000.0000.0410.1010.0920.0000.0360.1100.0850.0000.0000.1620.0690.0100.0000.0070.0000.0000.0000.0420.0000.0290.0000.0000.0000.0000.0110.0290.0000.0000.0000.0000.0000.0000.2170.0000.1240.0350.0000.0000.0440.0820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0580.0350.0000.0000.0290.0670.0670.0060.0570.0000.0000.0560.0000.1170.0130.0000.0000.0000.0000.0000.0000.0000.0140.0170.0650.0440.0200.0370.0250.0000.0840.0420.0000.0000.0000.0000.0190.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1040.0000.0220.0000.1380.138
LotConfig_Corner0.0800.2280.0000.0460.0910.0840.0650.0360.0280.0000.0570.0330.0780.0370.0900.0380.0410.0550.0000.0830.0480.1100.0000.0000.0640.0760.0000.0000.0350.0000.0000.0430.0000.0630.0000.0250.0000.0060.0000.0000.0000.0220.0001.0000.1160.0760.0000.7500.0340.0000.0310.0000.0000.0040.0000.0000.0000.0000.0340.0000.0360.0000.0000.0000.0000.0620.0000.0000.0000.0370.0000.0310.0000.0760.0000.0000.0720.0610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0460.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0470.0390.0310.0000.0210.0240.0000.0000.0000.0100.0160.0000.0000.0410.0000.0380.0260.0000.0340.0000.0300.0000.0000.0260.0630.0490.0000.0000.0000.0080.0000.0320.0380.0000.0220.0000.0820.0000.0000.0570.0000.0000.0000.0000.0000.0000.0000.0370.0250.0000.0000.0000.0380.0040.0000.0000.0000.0180.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0410.0000.0000.0240.0000.0000.0160.0000.0520.0000.0000.0000.0000.0410.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0200.0130.0000.0000.0700.070
LotConfig_CulDSac0.0910.1090.1840.0310.0000.1690.1370.0920.0470.0430.0510.1140.1060.0000.0950.0480.0480.1200.0990.0820.0320.0450.0980.1220.0870.0000.0370.0650.0230.0460.0990.0800.0820.0000.0000.0250.0000.1230.1030.0410.0230.0900.0250.1161.0000.0300.0000.4170.0000.0000.0000.0380.0710.0240.0000.0000.0000.0210.0000.0790.0470.0000.0250.0780.0490.0660.0000.0230.0000.0000.0800.0000.0520.0620.0120.0090.0270.0000.0420.0000.0000.0000.0000.0000.0490.0170.1360.0470.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.1070.0000.0710.0270.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0200.0850.0000.0680.0000.0740.0000.0000.0000.0530.0000.0000.0240.0230.0000.0000.0000.0540.0500.0000.0040.0000.0000.0000.0790.0030.0000.0000.0000.0000.0080.0180.0730.0760.0000.0360.0000.0000.0320.0710.0200.0950.0000.0080.0310.0000.0140.0350.0000.0000.0380.0000.0000.0070.0000.0140.0000.0000.0000.0000.0480.0000.0150.0000.0000.0450.0450.0310.0000.0000.0000.0490.0000.0230.0550.0280.0000.0000.0000.0000.0000.0000.0000.0000.1140.0000.0000.0000.1070.0440.0200.0470.0000.0310.0380.0000.0390.0000.0180.0630.0000.0300.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3350.335
LotConfig_FR20.0000.0000.0000.0000.0000.1030.0750.0280.0380.0250.0000.0000.0000.0000.0000.0000.0000.0750.0040.0000.0000.0000.0750.0000.0000.0000.0000.0190.0590.0000.0000.0300.0370.0000.0000.0000.0000.0000.0000.0130.0000.0000.0130.0760.0301.0000.0000.2870.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0320.0000.0000.0000.0090.0000.0370.0000.0000.0090.0110.0000.0000.0930.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.0000.0000.0000.0000.0180.0230.0000.0000.0230.0000.0000.0000.0000.0470.0000.0000.0000.0070.0000.0000.0340.0350.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0380.0620.0000.0000.0000.0260.0000.0450.0600.0000.0000.0000.0000.0000.0000.0000.0000.0530.0000.0230.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0310.0310.0300.0000.0000.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0070.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0090.0000.0000.0140.0000.0000.000
LotConfig_FR30.0210.0420.0000.0270.0000.0000.0180.0000.0000.0250.0310.0300.0000.0000.0000.0000.0000.0490.0000.0000.0000.0430.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0320.0000.0000.0000.0460.0200.0000.0000.0000.0000.0000.0000.0001.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0710.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0520.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0040.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0230.0000.0210.0110.0000.0000.0000.0200.0000.0000.0160.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
LotConfig_Inside0.0920.1320.0820.0320.0060.0950.1010.0510.0000.0000.0530.0740.0840.0000.0900.0180.0430.1310.0860.0830.0700.0410.0370.0000.0540.0770.0000.0000.0000.0290.0110.0490.0930.0710.0000.0330.0120.0890.0610.0380.0000.0000.0000.7500.4170.2870.0641.0000.0550.0000.0520.0000.0000.0000.0000.0000.0350.0000.0550.0420.0000.0000.0000.0790.0220.0000.0320.0000.0000.0000.0000.0090.0550.0890.0000.0000.0630.0530.0000.0000.0000.0000.0000.0000.0000.0190.0390.0230.0000.0000.0000.0000.0080.0000.0000.0000.0000.0070.0870.0000.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0680.0000.0560.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0180.0000.0000.0000.0000.0000.0000.0260.0000.0000.0070.0000.0300.0000.0000.0000.0000.0480.0360.0490.0000.0100.0000.0560.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0330.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0190.0340.0000.0000.0260.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0250.0000.0000.0180.0000.0000.0000.0000.0230.2550.255
Neighborhood_Blmngtn0.3980.0900.0000.1480.0610.1420.1450.0000.0000.3520.1580.1520.0520.0000.0270.1640.0000.1580.0180.1900.0000.0000.0000.0000.0000.0700.0470.1650.1120.0920.0000.0980.0780.0320.0000.0000.0000.0200.0000.0000.0000.0000.0000.0340.0000.0000.0000.0551.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2170.0000.0000.0000.3360.0060.0000.0990.0000.0000.0590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0260.0000.0000.0000.1240.0230.0000.0000.0000.0000.0000.0000.0000.0230.0000.0250.0000.0000.0000.0000.1270.0210.0000.0000.1410.1080.0000.0000.0000.1440.1300.0000.0000.0000.0000.0160.0000.0850.1120.0000.0000.0000.0000.0000.0910.0870.0000.0000.0000.0000.0650.0000.0000.0410.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0970.0000.0300.0000.0570.0000.0000.0000.0000.0000.0000.0000.0000.0000.1100.1000.0000.0000.0000.0000.0000.0000.0000.0000.0790.0000.0000.0000.0520.1680.0400.0810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0660.0000.0330.0000.0000.0000.0000.0000.0640.0710.071
Neighborhood_Blueste0.1600.0650.0000.0000.0330.0380.1090.0000.1410.0000.0000.0770.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0410.0570.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0000.0410.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Neighborhood_BrDale0.4910.2530.0000.0850.0000.2350.3620.0330.0000.0000.1500.2870.2760.0000.1090.0210.0000.1890.1750.0000.0000.0000.0000.0000.0000.0000.0420.0000.0640.1330.0000.0870.1110.0000.0000.0000.0000.1930.2410.0000.0000.0000.0000.0310.0000.0000.0000.0520.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2270.0000.0000.4290.0490.0000.0000.0950.0000.0000.1500.0000.0000.0000.0400.0000.0350.0000.0000.0000.0000.0000.0000.0000.0080.2010.0000.0230.0000.0000.0000.0660.0200.0000.0000.0000.0000.0000.0000.0100.1910.0170.0220.0000.0000.0000.0000.0640.0180.0000.0000.1350.1030.0000.0000.0000.0620.0710.0000.0000.0000.0000.0130.0000.1100.0830.0000.0000.0000.0000.0000.0790.1080.0000.0000.0000.0000.0240.0000.0000.0650.0280.0310.0530.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0830.0000.0000.0000.1100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0610.0810.0000.0000.0460.0000.0000.0000.0000.0000.0910.0000.0000.0000.1200.0450.0540.1020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0680.068
Neighborhood_BrkSide0.2630.0910.0000.1660.1550.3800.2260.0790.0000.0920.1200.1210.2070.0310.1160.0380.1210.2720.1770.0890.0650.0750.0000.1000.0000.0570.0680.0000.1790.1000.0000.0410.1580.0390.0000.0240.0000.1460.2030.0220.0000.0000.0000.0000.0380.0000.0000.0000.0000.0000.0001.0000.0000.0570.0130.0400.0310.0000.0000.0100.0780.0000.0280.0000.0300.0450.0000.0290.0200.0350.0000.0000.0000.0610.0000.0140.0000.0460.2080.1760.0520.0000.0000.0810.0000.0240.0000.0640.0340.0740.0000.0000.0770.0000.0000.0000.0000.0000.0560.0000.0710.0440.0000.0000.1070.1520.0000.0440.0000.0000.0000.0000.0000.0510.0000.0750.0000.0000.0000.0000.1040.1270.0600.0000.1130.1420.0500.0140.0650.1080.1020.0000.0830.0000.0560.0640.2990.0630.1110.0000.0000.0000.0480.0000.1400.1510.0230.0000.0000.0140.0660.0220.0000.0580.0180.0000.1060.0000.0000.1080.0000.0000.0000.0050.0160.0030.0560.0730.0000.0560.0000.0000.0800.0150.0380.0000.0230.1070.1070.1230.0260.0000.0000.1280.0220.1710.1100.0670.0220.0000.0160.0000.0000.0000.0230.0000.2210.0000.0130.0000.2220.0900.0990.1290.0000.0440.0000.0000.1080.0000.0000.0000.0000.0960.0000.0000.0000.0000.0000.0000.0480.0000.0580.0000.0000.0000.0000.0470.0490.0330.033
Neighborhood_ClearCr0.0480.0500.4120.0000.0000.0850.0550.0000.1720.0000.0810.0580.0630.0270.0850.1400.0000.0950.0000.1360.0240.1080.0500.0550.0000.0570.0800.0670.0540.0000.0000.1600.0450.0000.0000.0000.0000.0610.0440.0000.0000.3800.2240.0000.0710.0000.0000.0000.0000.0000.0000.0001.0000.0290.0000.0100.0000.0000.0000.0000.0460.0000.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0490.0000.0000.0000.0170.0320.0000.0000.0000.0000.0000.0000.0150.2780.0310.0000.0000.0000.0570.0000.0000.0000.0000.0000.0000.0000.0000.0000.1200.0000.0000.0840.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1280.0000.0000.0820.0480.0000.0540.0200.0140.0000.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0580.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1870.0000.1210.0000.0080.0000.0390.0000.0450.0430.0130.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0910.0000.0000.0870.0000.0640.0000.0000.0000.0490.0190.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0700.070
Neighborhood_CollgCr0.1760.0930.0000.2190.1870.3430.2800.0670.0230.1200.1240.0640.1650.0000.1000.1800.1260.3090.1510.1400.0000.1090.0000.0000.0000.0920.0530.0380.1390.0000.0590.1070.1390.0000.0000.0620.0000.1140.0710.0490.0370.0000.0680.0040.0240.0000.0000.0000.0010.0000.0000.0570.0291.0000.0520.0830.0710.0400.0010.0500.1390.0000.0680.0430.0700.0900.0250.0680.0580.0750.0250.0410.0000.0820.0330.0530.0450.0000.1090.0000.0670.0000.0000.0200.0000.0230.0000.0910.0000.0740.0000.0000.0150.0000.0000.0510.0000.0170.0520.0000.1180.0780.0000.0250.3510.1310.0000.0150.0000.0000.0250.0000.0150.0580.0000.1150.0000.0800.0000.0260.3580.1280.0000.0000.0680.0600.0000.0530.0000.2780.2380.0000.0290.0320.0000.0550.1060.1970.2810.0230.0000.0000.0190.0370.3050.2490.0470.0000.0000.0430.2180.0000.0000.1510.0580.0840.2010.0180.0750.0000.0000.0000.0000.0320.0250.0770.0000.0340.0090.0000.0000.0000.2390.0500.1140.0000.1390.0810.0810.0800.0280.0000.0000.0970.0000.0420.2490.2110.0000.0000.0330.0360.0000.0000.0830.0000.1450.0120.0000.0000.1110.0000.2030.1430.0000.0500.0000.0000.0840.0000.0160.0000.0000.0760.0450.0000.0000.0000.0000.0000.0310.0000.0000.0550.0000.0000.0150.0180.0270.0300.030
Neighborhood_Crawfor0.0640.0000.0000.0170.1820.2500.1040.0530.0000.0660.0000.0800.0300.0000.0880.0000.0970.2800.1540.0000.0000.0660.0710.0790.0000.0740.0000.0000.0000.0000.0000.2070.0790.0500.0000.0180.0210.0410.0340.0940.1980.0000.1870.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0521.0000.0360.0270.0000.0000.0000.0710.0000.0230.0000.0260.0400.0000.0240.0140.0300.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0350.0000.0000.0190.0000.0180.0000.0150.0400.0000.0000.0000.0000.0000.0000.0940.0000.0000.0480.0000.0130.0000.0350.0710.1180.0960.0000.0000.0000.0000.0000.0000.0030.0300.0000.0180.0000.0000.1460.1270.1160.0550.0080.0000.0200.0310.0000.0000.0000.0100.0140.0000.0000.0880.0000.0790.1270.0000.0630.0000.0000.0000.0000.0170.0160.0250.0000.0000.0000.0000.0480.0000.0230.0310.0000.0120.0520.0000.0710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0540.0540.0000.0000.0800.0000.0800.1060.0000.0760.0000.0190.0110.0000.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0240.0000.0000.0360.0000.0000.0280.0370.037
Neighborhood_Edwards0.1550.0760.0000.2340.2000.1840.1730.1440.0000.0490.1720.1310.0900.0000.1810.0000.0570.1920.2200.0670.0620.0000.0000.0000.1040.0800.1010.0520.1210.1040.0880.1380.2270.0000.0000.0450.0000.0660.0410.0130.0120.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0400.0100.0830.0361.0000.0530.0230.0000.0340.1090.0000.0500.0270.0520.0690.0000.0500.0410.0560.0000.0240.0000.0000.0000.0510.0000.0210.0930.0000.0000.0000.0000.0900.0220.0000.0380.0000.0000.0000.0000.0000.0030.0360.0960.0120.0000.0000.0430.0000.0500.0000.0000.0750.0800.0600.0720.0000.0000.0300.0000.0000.0000.0450.0000.0360.0000.0000.0000.0720.0700.0750.0000.0000.0840.0800.0000.0000.0000.1410.1400.0000.0440.0000.0000.0080.0530.0290.0940.0780.0000.0000.0080.0480.1110.0600.0640.0300.0050.1530.0340.0000.0070.0200.0000.0290.0810.0000.0180.0140.0000.0000.0000.0000.0000.0340.0000.0550.0170.0300.0000.0000.0360.0570.0000.0000.0000.1180.1180.0490.1310.0000.0000.1220.0000.0590.1340.1170.0950.0000.0000.0510.0000.0000.0660.0000.1700.1460.0120.0000.0690.0480.1070.0550.0000.0000.0000.0000.1530.0000.0270.0160.0000.1890.0000.0000.0000.0160.0000.0000.0270.0000.0000.0000.1650.0000.0000.0000.0290.0990.099
Neighborhood_Gilbert0.2860.1720.0000.1810.1580.2610.1910.0700.0190.1260.0650.1060.2920.0000.1700.1280.1570.2540.1820.1490.0000.0600.1020.0400.0000.0000.0650.0000.2100.2170.0270.1700.1760.0480.0000.0360.0000.1170.0920.0000.0750.0110.0000.0000.0000.0000.0000.0350.0000.0000.0000.0310.0000.0710.0270.0531.0000.0120.0000.0250.0940.0000.0400.0180.0420.0580.0000.0410.0320.0470.0000.0140.0000.0900.0000.0270.0200.0580.0620.0000.1660.0000.0000.2320.0120.0350.0000.0740.0000.0630.0000.0000.0000.0000.0000.0260.0000.0330.0000.0000.0750.0430.0000.0000.2120.0710.0000.0000.0000.0000.0000.0000.0330.0000.0000.0730.0000.0580.0000.0000.2160.0670.0140.0000.0260.0610.0400.0000.0000.0870.0740.0000.0000.0590.0000.0710.0700.1930.2390.0000.0000.0000.0210.0090.2260.1850.0220.0130.0000.0000.0000.0000.0320.0000.0570.0710.0000.0230.0540.1520.0000.0010.0000.0220.0290.0790.0000.0000.0000.0000.0000.0000.0700.0250.0900.0000.1420.0360.0360.0500.0000.0000.0000.0630.0390.0150.0680.0180.0000.0000.0000.0060.0000.0000.0530.0000.0300.0000.2240.0000.1310.2640.0000.1780.0000.0240.0000.0000.0710.0000.0090.0000.0000.0660.0200.0000.0000.0000.0000.0000.0470.0000.0000.0390.0000.0000.0000.0000.0440.2160.216
Neighborhood_IDOTRR0.1620.0530.0000.1940.1790.3080.2250.0880.0000.0760.0930.1080.1390.1190.1130.0650.0440.2010.1610.0860.0750.2140.0000.0000.0000.0000.0790.0130.1480.0610.0000.0990.1760.0000.4350.0000.0000.3050.2680.0800.0000.0000.0630.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.0230.0121.0000.0000.0000.0570.0000.0070.0000.0110.0280.0000.0080.0000.0170.0000.0000.0000.0150.0000.0000.0000.0290.1630.0440.0340.0000.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0700.0000.0000.0000.0000.0000.0560.0000.0670.0000.0000.0000.0730.0870.0000.0700.0000.0000.0130.0000.0000.0530.0000.0700.0000.0160.0000.0000.0810.0790.0000.0000.0890.1180.0330.0000.1840.0970.0620.0000.1820.0000.0000.0890.0800.0000.0830.0000.0000.0000.0310.0000.1220.1470.0810.0000.0460.0290.0000.0120.0000.0160.0000.0000.0840.0000.0000.0880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0860.0160.0000.0750.0580.2120.2120.1240.0870.0000.0750.1740.0230.1190.0790.0540.0000.0090.0000.0000.0000.0000.0000.0000.1720.0000.0180.0660.1330.0710.0640.0770.0310.0000.0000.0310.0930.0460.0700.0000.0000.1500.0000.0000.0000.0660.0000.0000.0310.0000.0000.0620.0000.0000.0000.0000.0320.0870.087
Neighborhood_MeadowV0.3460.2230.0000.1970.0710.2430.2430.0000.1010.1020.1210.1920.1120.0000.1090.1850.3350.1750.1340.1850.0000.0000.0000.0000.0000.0000.0000.0000.0840.0000.0000.0000.1330.0000.0000.0000.0000.2000.2490.0000.0000.0000.0000.0340.0000.0000.0000.0550.0000.0000.0000.0000.0000.0010.0000.0000.0000.0001.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2350.0000.0000.2250.1930.0060.0000.0710.0000.0000.0190.2040.0000.0000.0420.0000.0370.0000.0000.0000.0000.0000.0000.0000.5030.0260.0000.0260.0000.0000.0000.0690.0230.0000.0000.0000.0000.0000.0000.5080.0230.0000.0250.0000.0000.0000.0000.0670.0210.0000.0000.0440.0660.0000.0000.0000.0650.0740.0000.0000.0000.0000.0000.0000.0610.0290.0000.0000.0000.0000.0000.0490.0040.0000.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0840.0000.0000.0290.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0000.0000.0000.0000.0000.0000.0520.0000.0000.0000.0000.0600.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0710.071
Neighborhood_Mitchel0.1590.0000.0730.0730.0000.1390.2000.0780.0000.0660.0000.0000.0770.0000.0380.0000.0750.1420.0480.0000.0120.0000.1220.0000.0550.0000.0840.0000.0180.0970.0770.0650.1270.0000.0000.0170.0000.0370.0000.0000.0000.0760.0170.0000.0790.0320.0000.0420.0000.0000.0000.0100.0000.0500.0000.0340.0250.0000.0001.0000.0700.0000.0220.0000.0240.0390.0000.0220.0110.0280.0000.0000.0000.0370.0000.1150.0000.0000.0000.0000.0480.0000.0000.0910.0740.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1140.0000.0450.0500.0000.0000.0000.0650.0000.0000.0000.0000.0000.0000.0000.0550.0960.0550.0000.0690.0000.0000.0000.0630.0000.0000.0000.0230.0000.0000.0000.0840.0990.0000.0000.0200.0000.0130.0490.0300.0000.0000.0000.0210.0400.0000.0310.0000.0000.0000.0000.0000.0590.0000.0000.0540.1150.0000.0000.0220.0000.0520.0000.0000.0000.0340.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.0440.0320.0320.0000.0000.0000.0000.0260.0340.0000.0660.1020.0000.0000.0000.0000.0000.0000.0000.0730.0320.0000.0000.0000.0490.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0210.0210.0000.0000.0000.0070.0000.0260.0000.000
Neighborhood_NAmes0.2740.1460.0000.2840.1530.5770.4480.0930.1760.1030.0430.1310.2060.0000.2090.0830.1720.4900.1410.1540.1050.0380.0610.0350.0000.0530.0440.0820.2910.1120.0200.1090.2300.0000.0000.0840.0000.2080.1740.0520.0350.0560.1000.0360.0470.0000.0000.0000.0270.0000.0240.0780.0460.1390.0710.1090.0940.0570.0270.0701.0000.0000.0900.0610.0930.1170.0410.0910.0790.0990.0410.0580.0010.0800.0340.0830.0640.1030.0440.0000.1750.0000.0010.1880.0000.0930.0150.0650.0000.0640.0000.0000.0000.0000.0000.1420.0000.0700.0280.0000.1170.0000.0000.0000.1960.0580.0000.0000.0000.0000.0940.0000.0690.0450.0000.1120.0000.0000.0000.0000.1990.1030.0000.0740.0650.0270.0630.0500.0190.2430.2680.0000.0000.0000.0000.0000.1300.4000.3280.0330.0000.0000.1220.0250.2820.3480.0000.0540.0000.0100.0890.0000.0000.0610.0840.1910.1740.0000.1830.1680.0000.0780.0190.1100.1500.1900.0000.0000.0000.0000.0000.0630.2170.0000.0520.0000.1840.0390.0390.0890.0000.0000.0000.0630.0780.0140.1870.2390.0000.0000.0980.0220.0000.0000.0380.0000.0450.0000.0670.0070.0000.1200.0070.1020.0000.0570.0000.0000.0800.0000.0250.0000.0000.0540.0730.0000.0000.0000.0000.0000.1230.0000.0570.0450.0000.0110.0000.0580.1250.0480.048
Neighborhood_NPkVill0.2320.0730.0000.1090.0400.1680.2410.0000.0000.0000.0000.0360.0740.0000.0000.0000.0000.1270.0760.0000.0000.0000.0000.0140.0000.0510.0170.0000.0590.0000.0000.0290.0450.0250.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1640.0000.0000.1130.1540.0000.0000.0000.0000.0000.0210.0000.0000.0000.0170.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2610.0000.0000.0410.0000.0000.0000.0000.5640.0000.0000.0000.0000.0000.0000.0000.0730.0000.0000.0400.0000.0000.0000.0340.0500.0000.0000.0000.0380.0460.0000.0000.0000.0000.0000.0000.0770.0560.0000.0000.0000.0000.0000.0400.0040.0000.0000.0000.0000.0000.0000.0000.0410.1480.0000.0310.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0660.0000.0000.0000.0900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0450.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Neighborhood_NWAmes0.1160.1180.0000.1580.1520.3670.2910.0850.0940.0000.0790.0870.0650.0000.0940.1280.1120.3720.2190.0670.0630.0000.0570.0670.1180.0420.0000.1040.1220.0950.0000.1370.1880.0000.0000.0330.0000.1120.0880.0320.0230.0030.0670.0000.0250.0000.0000.0000.0000.0000.0000.0280.0000.0680.0230.0500.0400.0070.0000.0220.0900.0001.0000.0140.0390.0550.0000.0380.0290.0430.0000.0090.0000.0760.0000.0000.0160.0550.0580.0000.0000.0000.0000.0340.0000.0420.0000.0420.0000.0320.0940.0000.0000.0000.0000.0000.0000.0000.1410.0000.0410.1790.0000.0000.1040.0650.0000.0000.0000.0000.0000.0000.0000.1520.0000.0380.0000.1610.0000.0000.1000.0520.0090.0000.1840.1430.0190.0000.0000.0820.0950.0000.0000.0210.0000.0000.0660.2000.1420.0000.0000.0000.0450.0000.0240.0000.0000.0000.0000.0000.0300.0500.0000.0810.1520.0000.0440.0000.0000.0320.0000.0000.0000.0900.0580.0710.0000.0000.0000.0000.0000.0000.1460.0240.0130.0000.1150.0470.0470.0470.0000.0000.0000.0590.0170.0110.0160.0550.0000.0000.0000.0000.0000.0000.0000.0000.1580.0000.0000.0000.1170.0000.0820.0420.0000.0210.0000.0000.0440.0000.0000.0000.0000.0620.0570.0000.0000.0000.0000.0000.0580.0000.0130.0000.0000.0000.0310.0140.0590.0170.017
Neighborhood_NoRidge0.2110.1600.0000.2920.0710.3410.3010.1640.0000.0980.1920.1900.3950.0000.3880.1900.2560.3760.2360.1520.1750.0080.0000.1380.0660.0190.0860.0000.1610.1860.0000.1480.2630.0000.0000.0050.0000.0790.0600.0000.0000.0000.0260.0000.0780.0090.0000.0790.0000.0000.0000.0000.0000.0430.0000.0270.0180.0000.0000.0000.0610.0000.0141.0000.0160.0320.0000.0140.0000.0210.0000.0000.0000.0650.0000.0000.0000.0330.0000.0000.1120.0000.0000.1780.0000.0050.0000.0490.0000.0620.0000.0000.0000.0000.0000.0000.0000.0000.0270.0700.0170.0300.0000.0000.0650.0070.0000.0000.0000.0000.0000.0000.0000.0350.1080.0130.0000.0410.0000.0000.0680.0410.0000.0000.1780.1300.0370.0000.0000.2160.2120.0000.0000.0000.0000.0000.0420.1340.1760.0000.0000.0000.0180.0000.1420.1460.0000.0000.0000.0000.0000.0290.0000.0000.0600.0430.1980.0000.0390.0430.0000.0000.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.1360.0000.0250.0000.1020.0250.0250.0250.0000.0000.0000.0360.0000.0000.1500.1480.0000.0000.0000.0000.0000.0000.0270.0000.0990.0000.0000.0000.0940.0890.0550.1190.0000.0000.0000.0000.0260.0000.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0350.0000.0540.0000.0000.0000.0000.0160.0360.1130.113
Neighborhood_NridgHt0.2040.2120.0000.4910.1950.3440.3420.2100.0000.2650.3280.2730.1290.0000.1830.0260.1750.3750.3630.1780.1290.0580.0280.0000.0000.0580.0620.0440.2000.0000.0260.1810.3870.0000.0000.0350.0000.1080.0820.0340.0000.0090.0590.0000.0490.0000.0710.0220.0000.0000.0000.0300.0000.0700.0260.0520.0420.0110.0000.0240.0930.0000.0390.0161.0000.0570.0000.0400.0310.0460.0000.0120.0000.0680.0000.0260.0720.0930.0710.0000.0700.0000.0000.0000.0110.0350.0000.1210.0000.1400.0000.0000.0000.0000.0000.0250.0000.0430.0920.0000.0050.0550.0000.0000.2120.0870.0000.0000.0000.0000.0000.0000.0440.0880.0000.0000.0000.0670.0000.0000.1910.0850.0400.0000.0610.2740.3650.4090.0000.1390.2910.0000.0000.0690.0000.0790.0690.1960.2540.0000.0000.0000.4560.0060.0000.2060.0200.0000.0000.0000.0890.0630.0000.1050.0730.0580.1840.0400.0640.0000.0000.0000.0000.0210.0280.0680.0000.0000.0000.0000.0000.0000.2160.0240.0800.0000.1460.0500.0500.0490.0000.0000.0000.0620.4020.0140.0320.2330.0000.0000.0000.0020.0000.0000.0520.0000.0740.0000.1640.0000.1360.1260.1070.1870.0000.0230.0000.0000.0700.0000.0060.0000.0000.0650.0180.0000.0000.0000.0000.0000.2760.0000.2090.0520.0000.0000.0000.1390.2720.0210.021
Neighborhood_OldTown0.2460.0770.0000.1650.2730.5590.2170.1550.0000.1410.1420.1350.1560.1620.0340.1440.0960.3350.1610.1100.1170.2770.0000.0500.0000.0000.1490.0000.1210.1130.1920.1350.1370.0000.0000.0500.0000.4490.5580.0790.0000.0270.0000.0620.0660.0370.0000.0000.0000.0000.0000.0450.0170.0900.0400.0690.0580.0280.0000.0390.1170.0000.0550.0320.0571.0000.0110.0550.0460.0620.0110.0290.0000.0000.1960.0000.0340.0750.1700.0000.1130.0600.1950.0000.0000.0500.0000.0000.0410.0340.0240.0000.0830.0000.0000.0000.0320.0320.0940.0000.1080.0620.0000.1070.1120.1420.0000.0830.0000.0000.0000.0320.0310.0880.0000.1050.0000.0780.0000.1230.1070.1420.0000.0000.1700.2100.0720.0230.0270.1570.1580.0670.0000.1190.0000.1200.3600.0290.1770.0080.0770.0000.0780.2630.2080.1750.0700.0350.0000.0000.1160.0740.0000.1420.0390.0430.1560.0620.0000.1850.0000.0000.0000.0150.0000.0380.0000.1560.1390.0240.0900.0000.1140.0620.1200.0000.0000.1870.1870.1360.0000.0670.0000.1410.0000.0000.1110.1180.0000.0000.0000.0790.0220.0000.0630.0000.2590.0000.0510.0000.2920.1460.1690.2570.0000.2980.0270.0000.2520.0000.1960.0000.0680.1770.0000.0000.0000.0030.0000.0000.0690.0000.0320.0260.0000.0000.0340.0000.0600.1460.146
Neighborhood_SWISU0.1750.0350.0000.1080.0590.2760.1380.0540.0000.0480.0410.0000.1260.3470.1140.2490.2110.2240.1550.0270.0180.1490.0000.0000.0000.0430.0000.0000.0000.0000.0000.0290.1360.0280.0000.0000.2130.0000.0400.2430.0000.0000.1360.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0111.0000.0000.0000.0000.0000.0000.0000.0000.0670.0000.0000.0110.1500.0000.1020.2390.0000.0000.0000.0000.0000.0430.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0070.0000.0000.0260.1340.0330.0000.0000.0000.0000.0000.0000.0380.0000.0000.0000.0000.0000.0000.0380.1230.0100.0000.0650.0900.0180.0000.0000.0600.0600.0000.0290.0000.0000.0000.1740.0740.0000.0000.0000.0000.0150.0000.0600.0960.1110.0000.0000.0440.0210.0200.0000.0510.0000.0000.0610.0470.0310.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1010.1010.0000.0310.0000.0000.0350.0000.1230.0080.0000.0000.0270.0000.0190.0000.0000.0000.0000.1420.0000.0000.0000.1150.0490.0480.0600.0000.0000.0000.0000.0640.0000.0000.0000.0000.0520.0000.0000.0000.0000.0000.0000.0150.0000.0350.0000.0000.0000.0000.0000.0170.0680.068
Neighborhood_Sawyer0.1600.0720.0000.2440.0410.3280.2560.0000.0960.1000.0630.0000.1130.0000.1410.1260.1060.2150.0800.0000.0820.0000.0000.0000.0000.0000.0000.0000.1500.0640.0000.0400.1170.0580.0000.0330.0000.0970.0700.0000.0230.0050.0570.0000.0230.0000.0000.0000.0000.0000.0000.0290.0000.0680.0240.0500.0410.0080.0000.0220.0910.0000.0380.0140.0400.0550.0001.0000.0300.0440.0000.0100.0000.0290.0000.0410.0170.0560.0000.0000.0950.0000.0000.1130.0670.0000.0000.0400.0000.0490.0000.0210.0000.0000.0000.0230.0000.0000.1830.0000.0180.0400.0000.0000.0850.0000.0000.0000.0000.0000.0000.0000.0300.1410.0000.0140.0000.1160.0000.0000.0880.0000.0100.0810.0220.0410.0480.0250.0000.1450.1630.0000.0000.0340.0000.0200.0560.1960.1570.0000.0000.0000.0580.0000.1290.1780.0190.0000.0000.0170.0000.0380.0000.0310.0490.0560.0620.0000.0690.0660.0000.0530.0000.0000.0000.0380.0000.0000.0000.0000.0000.0000.0970.0000.0000.0000.1190.0130.0130.0480.0000.0000.0000.0600.0500.0000.1130.1490.0000.0000.0000.0950.0000.0000.0620.0000.0000.0000.0000.0320.0000.0630.0310.1030.0000.0000.0000.0000.0170.0000.0000.0000.0000.0240.0170.0000.0000.0000.0000.0000.0590.0000.0720.0000.0000.0000.0000.0390.0600.0110.011
Neighborhood_SawyerW0.0890.0000.0000.0850.0780.3340.2430.0000.1150.0230.0000.0000.0370.0000.0000.0000.0000.2190.0590.0960.0000.0000.0000.0300.0000.0000.0300.0000.0790.0250.0000.0000.0740.0480.0000.0250.1260.0530.0770.0000.0130.0380.0000.0000.0000.0090.0000.0000.0000.0000.0000.0200.0000.0580.0140.0410.0320.0000.0000.0110.0790.0000.0290.0000.0310.0460.0000.0301.0000.0350.0000.0000.0000.0000.0000.0370.0000.0000.0470.0000.0000.0000.0000.0420.0000.0000.0000.0560.0000.0460.0000.0000.0000.0000.0000.0130.0000.0220.0890.0000.0670.1320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0770.0000.0650.0000.1120.0000.0000.0000.0240.0000.0000.0000.0000.0200.0150.0000.0910.0750.0000.0000.0290.0000.0120.0440.0340.0520.0640.0000.0000.0340.0000.1560.1430.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0930.0000.0390.0450.0000.0000.0640.0000.0170.0200.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0200.0200.0190.0000.0000.0000.0360.0410.0000.1160.0730.0000.0000.0000.0000.0000.0000.0230.0000.0610.0000.0000.0000.0310.0150.0420.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0480.0000.0000.1130.0000.0000.0360.0000.000
Neighborhood_Somerst0.2780.0760.0000.2600.1900.3650.3400.0340.0300.1910.0510.1240.0920.1680.0870.0480.0650.3390.2110.0000.2220.0660.0000.0000.0000.0000.0300.0490.2060.1060.0320.0600.1740.0000.0000.8560.0000.3290.0970.0380.0290.0150.0750.0370.0000.0110.0000.0000.0000.0000.0000.0350.0000.0750.0300.0560.0470.0170.0000.0280.0990.0000.0430.0210.0460.0620.0000.0440.0351.0000.0000.0180.0000.0930.0060.0310.0990.1140.0770.0000.0160.0000.0000.1380.0170.0390.0000.0600.0000.0470.0000.0000.0000.0000.0000.0290.0000.0970.0990.0000.0730.0600.0000.0000.1260.0940.0000.0000.0000.0000.0000.0000.0990.0940.0000.0770.0000.0730.0000.0000.1250.0910.0180.0000.0560.0560.1930.0000.0000.2750.2740.0000.0000.0740.0000.0850.0740.2150.2760.0000.0000.0000.0620.0140.2050.2190.0250.0000.0000.0000.0000.0590.0150.0000.0810.0540.1050.0440.0580.0800.0000.0110.0000.0260.0320.0930.0000.0080.0000.0000.0000.0000.2070.0280.0540.0000.1560.0540.0540.0540.0000.0000.0000.0670.0320.0190.2180.2240.0000.0000.0060.0120.0000.0000.0560.0000.0410.0000.0000.0000.0000.0380.1040.1100.0000.0280.0000.0000.0750.0000.0140.0000.0000.0700.0230.0000.0150.0000.0000.0000.2540.0000.1970.0000.0000.0000.0000.1580.2600.0480.048
Neighborhood_StoneBr0.2080.0590.0050.2560.0980.1520.1540.1620.0400.0590.1540.1260.1370.0000.0770.1180.0900.1140.1420.0940.0000.0000.0000.0740.0000.1000.0850.0000.0920.0000.0000.0950.1160.0000.0000.0000.0000.0560.0400.0000.1320.0150.0820.0000.0800.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0110.0000.0000.0000.0001.0000.0000.0000.1020.0000.0000.0000.1860.0260.0000.0470.0000.0000.0000.0000.0000.0000.0440.0000.0550.0000.0000.0000.0000.0000.0000.0000.1420.0480.0000.0210.0070.0000.0000.0000.0380.0000.0000.0000.0000.0000.0000.1160.0540.0000.0190.0940.0220.0000.0000.0000.0360.0000.0000.0000.0000.0560.0700.0000.1330.1610.0000.0000.0000.0000.0000.0240.0740.1080.0000.0000.0000.1200.0000.0460.1100.0000.0240.0000.0000.0480.0310.0160.0830.0000.0240.1180.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.0000.0500.0000.0000.0000.0000.0000.0000.0170.1390.0000.0530.1250.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0610.0000.0680.1140.0000.1020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.1000.0000.0770.0000.0000.0000.0000.0490.0980.0990.099
Neighborhood_Timber0.0610.0730.1960.1540.0610.1120.1170.1230.0610.0770.1080.1360.0000.0000.0710.0000.0450.0910.1100.1050.0000.0000.0000.0000.0000.0000.0760.0000.1120.0000.0000.1010.1420.0000.0000.0000.0000.0750.0570.0000.1920.0000.1050.0310.0000.0000.0000.0090.0000.0000.0000.0000.0000.0410.0000.0240.0140.0000.0000.0000.0580.0000.0090.0000.0120.0290.0000.0100.0000.0180.0001.0000.0000.0480.0000.0000.0000.0300.0000.0000.0170.0000.0000.0000.0000.0270.0000.0700.0000.0830.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0450.0000.0780.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0710.0370.0000.0000.0690.0830.0200.0000.0000.0850.0850.0000.0000.0000.0000.0100.0390.0000.0410.0000.0000.0300.1120.0000.0390.0950.0000.0000.0000.0000.0370.1470.0000.1080.0000.0000.0570.0000.0130.0000.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0220.0220.0220.0000.0000.0000.0000.0000.0190.0000.0580.0700.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0790.1210.0000.1040.0000.0000.0000.0000.0210.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0450.0000.0360.0000.0000.0000.0000.0150.0430.1000.100
Neighborhood_Veenker0.0710.0000.0790.0000.0000.1300.0880.1000.1760.0000.0570.0740.0000.0000.0000.1240.0000.0730.0550.2950.0000.0000.0000.1100.0000.0000.1460.0000.0840.0540.0000.0680.0360.0440.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0520.0930.0000.0550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0410.0000.0000.0190.0000.0000.0180.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0890.0000.0000.0000.0000.0000.1080.0000.0000.0500.0000.0000.0000.0000.0000.1390.0000.0000.0000.0000.0000.0000.0880.0000.0000.0480.0000.0000.0000.0180.0400.0000.0000.0000.0590.0480.0000.0000.0260.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0370.0280.0000.0000.0000.0000.0000.1200.0000.0360.0310.0000.0000.0220.0000.0150.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0580.0000.0000.0000.0350.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0690.069
BldgType_1Fam0.9880.4400.0000.0370.1830.2400.2090.0000.0000.1740.1930.2240.1320.0940.0990.3800.2340.2060.1630.1020.0700.0710.0000.0000.0000.0170.1260.0820.1340.1380.4460.1250.1930.0000.0000.1390.0640.2830.2200.0000.0000.0120.0000.0760.0620.0000.0140.0890.2170.0520.2270.0610.0490.0820.0000.0000.0900.0150.2350.0370.0800.1640.0760.0650.0680.0000.0000.0290.0000.0930.1020.0480.0001.0000.3250.4280.3870.6520.0730.0000.0000.0000.0000.0270.1200.0380.0000.0610.0100.0520.0000.0000.0000.0000.0000.0000.0000.1410.0000.0000.0910.0930.0000.0000.0520.1100.0000.0000.0000.0860.0000.0000.1430.0000.0000.0920.0000.0000.0000.0290.0710.1040.0000.0250.0650.0730.0240.0000.0320.0250.0300.0000.0290.0720.0000.0490.0850.0000.0270.0920.0000.0000.0000.0000.1200.1400.0000.0000.0000.0340.0000.0000.0000.0530.0000.0400.1060.0000.0610.0470.0340.0000.0550.0000.0000.0000.0000.0000.0000.0000.0000.0600.0160.0000.0430.0000.0730.0770.0770.0270.0000.0320.0000.0000.0000.0000.0000.0000.0320.0000.0000.0270.0000.0000.0000.0350.0730.0000.0470.0660.0260.0000.0410.0000.0000.0490.0000.0000.0410.0000.0070.0000.0000.0480.0000.0000.0000.0400.0000.0000.0000.0000.0000.0000.0140.0890.0000.0000.0000.1450.145
BldgType_2fmCon0.8460.0000.1200.1180.1380.3260.0900.0000.0000.0000.0480.0000.0060.1400.0000.1890.1870.1700.1720.1000.0000.0460.0000.0000.0000.0270.0000.0000.0000.0870.3570.0490.1660.0000.0000.0000.0460.0890.0880.0940.0000.0000.0630.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.1960.0670.0000.0000.0060.0000.0000.0000.3251.0000.0000.0000.0220.0600.0000.0710.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0730.0000.0000.0000.0470.0500.0000.0350.0000.0000.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.0670.0540.0000.0000.0560.0740.0000.0000.1540.0960.0660.0000.2030.0000.0000.0790.0810.0000.0840.0000.0090.0000.0240.0820.0790.0590.0650.0000.0000.0260.0000.0000.0670.0270.0000.0260.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0880.0870.0000.0000.0000.0640.0000.0000.0000.0380.2390.2390.0410.0000.0380.0000.0590.0160.1050.0930.0700.0000.0000.0000.0000.0490.0000.0000.0090.1030.0000.0000.0000.0240.0360.0620.0240.0000.0000.0520.0380.1440.0000.0000.0000.0000.1400.0000.0000.0000.1380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0730.073
BldgType_Duplex0.9980.0000.0000.1700.1310.1880.2330.0000.0000.1680.1330.1060.0630.0000.0000.3890.1880.1870.1400.0640.0640.0000.0000.0000.0000.0000.2780.0860.1680.2960.7590.1710.2000.0000.0000.0190.0630.0000.0000.0000.0000.0000.0060.0000.0090.0000.0000.0000.0000.0000.0000.0140.0000.0530.0000.0510.0270.0000.0000.1150.0830.0000.0000.0000.0260.0000.0000.0410.0370.0310.0000.0000.0000.4280.0001.0000.0000.0410.0000.0000.0000.0000.0000.0440.1670.0000.0000.0230.0000.0460.0000.0340.0000.0600.0000.0000.0000.0160.0000.0000.0090.1340.0000.0000.0800.0310.0000.0000.0190.0000.0000.0000.0160.0000.0000.0000.0000.1280.0790.0000.0770.0280.0000.0000.0270.0000.0060.0060.0000.1300.1360.0000.0000.0210.0000.0190.0520.1160.1430.2500.0000.0000.0440.0000.0500.0000.0000.0000.0000.1340.0000.0400.0410.0770.0220.0380.0000.0000.0000.0160.0000.0000.0000.0000.0000.1060.0000.0220.0000.0000.0000.1650.1600.0250.0000.0000.1790.1190.1190.0000.0650.0190.0000.0610.0360.0000.1440.1630.0000.0000.0000.0000.0000.0000.0150.0700.1440.0000.0000.1480.0590.0830.1070.1090.0000.0000.0000.0000.0840.0000.0000.0000.0000.0950.0000.0000.0000.0000.0000.0000.0440.0190.0000.0000.0920.2470.0500.0420.0450.0520.052
BldgType_Twnhs0.6330.3910.0000.0820.0000.2040.2320.0190.0000.0180.1190.3150.2070.0400.0310.0970.1250.1000.0660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1050.0000.0870.0620.0000.0000.1270.0000.2310.1920.0070.0000.0000.0450.0720.0270.0410.0000.0630.0000.0410.4290.0000.0000.0450.0000.0000.0200.0000.2250.0000.0640.1130.0160.0000.0720.0340.0000.0170.0000.0990.0000.0000.0000.3870.0000.0001.0000.0340.0460.0000.1260.0000.0000.1690.0560.0000.0000.0620.0000.0550.0000.0000.0000.0000.0000.0000.0000.1330.0210.0000.0220.0000.0000.0000.0170.0590.0000.0000.0000.1320.0000.0000.1350.0100.0000.0260.0000.0430.0000.0000.0470.0570.0230.0000.0500.0950.0780.0000.0000.0060.0000.0000.0000.0000.0000.0000.0440.0170.0000.0000.0000.0000.0150.0000.0630.0140.0000.0000.0000.0000.0000.0220.0100.0580.0220.0000.0000.0000.0220.0000.0000.0000.0370.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0270.0270.0000.0000.0000.0380.0000.0000.0000.0000.0830.0000.0000.0000.0000.0000.0000.0000.0970.0000.0000.0000.0790.0700.0230.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0380.1080.108
BldgType_TwnhsE0.8580.4060.0000.1470.1660.2600.1870.0000.0320.1730.1390.1540.1260.0750.1140.4030.2640.2210.1580.1610.0000.0560.0000.0390.0000.0630.0360.0470.0720.0000.0460.0810.1450.0000.0000.1520.0000.2010.1370.0490.0000.0280.0450.0610.0000.0000.0520.0530.3360.0000.0490.0460.0170.0000.0000.0210.0580.0290.1930.0000.1030.1540.0550.0330.0930.0750.0110.0560.0000.1140.1860.0300.0410.6520.0220.0410.0341.0000.0920.0000.1030.0000.0000.0000.0000.0180.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.1470.0320.0000.0280.0300.0000.0110.0110.1110.0000.0000.0000.0240.0000.0000.1500.0220.0000.0330.0000.0000.0000.0140.0090.1080.0000.0000.0600.0730.0180.0210.0000.1790.1840.0000.0170.0620.0000.0770.0890.1260.1990.0090.0000.0000.0500.0270.2120.2150.0360.0000.0000.0230.0600.0000.0000.0330.0000.0630.1890.0000.0740.0440.0000.0000.0260.0000.0000.0000.0000.0230.0000.0000.0000.0000.1130.0390.0430.0000.0550.0670.0670.0660.0160.0000.0000.0810.0270.0310.1160.1190.0000.0000.0000.0250.0000.0000.0000.0000.1140.0000.0510.0000.0630.1230.0660.1470.0000.0380.0000.0000.0720.0000.0270.0000.0000.0750.0000.0000.0000.0000.0000.0000.0590.0000.0500.0000.0000.0000.0000.0000.0560.0350.035
HouseStyle_1.5Fin0.8910.0830.0990.1730.2480.5000.3600.1430.0000.1680.1510.1330.6340.1680.0850.0990.0730.3570.2600.1090.1070.1890.0790.0680.0000.0250.1340.0000.1310.0400.0000.0410.2470.0000.0610.0640.0000.1450.1950.0940.0000.0000.0430.0000.0420.0000.0000.0000.0060.0000.0000.2080.0320.1090.0270.0930.0620.1630.0060.0000.0440.0000.0580.0000.0710.1700.1500.0000.0470.0770.0260.0000.0000.0730.0600.0000.0460.0921.0000.0000.3380.0000.0000.2230.0410.0640.0000.1280.0230.1360.0000.0000.0800.0000.0000.0080.0210.0350.1150.0000.1240.0530.0000.0180.1460.1760.0580.0800.0000.0000.0000.0210.0340.1010.0000.1360.0000.0500.0000.0380.1460.1860.0410.0000.1650.2180.0990.0540.0000.1580.1770.0000.1200.0700.0000.1180.2900.0000.1990.0000.0000.0520.0870.0490.2050.2320.0830.0250.0000.0100.1140.0780.0000.1190.0550.0000.1690.0840.0770.1000.0000.0370.0000.0000.0000.0000.0000.1050.0890.0500.0000.0000.1210.0000.0800.0000.0490.0620.0620.1210.0720.0000.0000.1420.0660.0200.1310.1540.0000.0000.0000.0990.0590.0000.1020.0000.2270.0000.0240.0000.2450.1360.1750.2510.0000.0890.0000.0000.1060.0000.0310.0000.0000.0590.0000.0000.0000.0360.0000.0000.0960.0000.0520.0000.0380.0000.0000.0390.0980.1080.108
HouseStyle_1.5Unf0.2290.0000.0000.0000.0920.1680.1570.0000.0000.0650.0430.0690.0340.0000.1540.1080.1400.1420.1000.0360.0000.1030.0000.0000.0000.0000.0300.0000.1000.0670.0000.0000.0690.0510.0000.0000.0000.0740.0830.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1760.0000.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0870.0000.0000.0510.0000.0000.0000.0350.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0820.0000.0000.0000.0600.0660.0000.0000.0000.0000.0000.0000.0000.0140.0000.0840.0000.0000.0000.0000.0580.0910.0000.0000.0510.0700.0000.0000.0000.0390.0490.0000.0000.0000.0000.0000.1410.0000.0450.0000.0000.0000.0000.0000.0730.0850.0350.0000.0000.0000.0000.0000.0000.0420.0000.0000.0290.0000.0000.0620.0000.0000.0000.0000.0000.0140.0000.0510.0000.1430.0000.0000.0000.0310.0000.1290.0000.0690.0690.0380.1140.0000.0000.1040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0800.0000.0000.0000.0720.0390.0300.0460.0000.0320.0030.0680.1150.0000.0000.0000.1430.0990.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0260.0000.0460.046
HouseStyle_1Story0.9600.0000.0120.2230.0830.3170.2380.2180.0830.2710.3820.4100.8580.0920.3810.3750.3870.2750.0520.0070.0640.0770.0050.0000.0450.0000.1430.0000.1890.4890.0290.0790.0000.0000.0000.0880.0000.1930.1550.0460.0000.0420.0000.0000.0000.0000.0000.0000.0990.0000.0950.0520.0000.0670.0350.0000.1660.0340.0710.0480.1750.0000.0000.1120.0700.1130.1020.0950.0000.0160.0470.0170.0190.0000.0710.0000.1260.1030.3380.0871.0000.0590.0740.6570.1540.2100.0000.1770.0740.2060.0290.0000.0450.0000.0000.0760.0000.0020.0000.0000.0330.0240.0000.0000.0000.0000.0000.0450.0000.0000.0810.0000.0000.0000.0000.0280.0000.0930.0000.0240.0000.0000.0270.0090.0000.0590.1030.0220.0000.0000.0000.0000.0000.0000.0000.0210.1440.1190.0250.0420.0180.0150.0720.0000.0900.0190.0480.0000.0000.0000.0000.0270.0000.0390.0640.0820.0000.0000.0470.1730.0000.0000.0000.0450.0040.0830.0000.0320.0340.0000.0000.0000.0000.0000.0620.0000.0360.0180.0180.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0130.0520.0000.0000.0000.0000.2130.0240.2420.0000.0960.0000.0470.0180.0000.0410.0410.0000.0400.0000.0230.0000.0290.0210.0600.0000.0000.0000.0000.0000.0630.0160.0830.0000.0000.0000.0000.0600.0700.0000.000
HouseStyle_2.5Fin0.2030.0000.0000.0360.1470.2180.0650.0000.0000.0000.0000.0000.2820.6850.3920.4150.4220.0000.0320.0690.0890.0000.0000.1870.0000.0000.0000.0000.0330.0000.0000.0140.0520.0000.0000.0000.0260.0320.0220.1420.0000.0000.0780.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.2390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0591.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0860.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0290.0450.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0790.0000.0000.0000.0000.0000.0000.0000.0470.0480.0620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0380.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0190.0000.0000.0120.0000.0260.0000.0960.0000.0000.0000.0440.0000.0000.0470.0000.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.000
HouseStyle_2.5Unf0.2750.0000.0000.0280.0360.2920.0290.0000.0000.0430.0000.0000.1000.0000.0070.0000.1240.2660.0000.0000.2760.1840.0000.0880.0000.0000.0570.0000.0000.0000.2150.0000.0000.0000.0310.0000.0000.1170.1280.0750.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.1950.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0000.0000.0740.0001.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.0880.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0500.0620.0000.0880.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0480.0000.0540.0000.0420.0590.0000.0000.0190.0460.0080.0790.0000.0000.0000.0000.1400.0000.0470.0000.0000.0000.0000.0000.0230.0510.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0380.0000.0640.0500.0000.0000.0000.0000.0000.0000.0000.1190.0940.0000.1010.0000.0200.0420.0000.0000.0000.1840.1840.0000.0000.0000.0000.0350.0000.0000.0170.0170.0000.0000.0000.0000.0000.0000.0000.0000.0940.0000.0000.0000.1150.0300.0390.0750.0000.0430.0190.0000.0560.0000.0000.0000.0440.0310.0000.0000.0000.0000.0000.0570.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.000
HouseStyle_2Story0.9610.0520.0000.3350.1610.3350.2680.1350.0960.1620.2270.3190.8320.0750.4900.3740.4450.3170.1530.0910.1910.0390.0000.0290.0480.0160.1130.0080.3590.6370.0000.1400.2130.0000.0100.1910.0000.0880.0000.0330.0000.0340.0290.0000.0000.0000.0000.0000.0590.0240.1500.0810.0000.0200.0190.0900.2320.0480.0190.0910.1880.0210.0340.1780.0000.0000.0000.1130.0420.1380.0000.0000.0180.0270.0000.0440.1690.0000.2230.0510.6570.0290.0421.0000.0990.1370.0000.0810.0470.1000.0440.0000.0000.0000.0000.0840.0000.0250.0000.0000.0450.0400.0000.0000.1680.1050.0280.0000.0000.0440.0530.0000.0180.0000.0000.0510.0000.0850.0000.0120.1670.1220.0000.0000.0830.0740.0000.0000.0330.2150.2090.0000.0200.0480.0000.0620.0530.2200.2630.0360.0290.0000.0000.0000.2340.2270.0000.0320.0000.0000.0560.0520.0000.0930.0730.0670.0610.0220.0840.1390.0000.0490.0000.0390.0390.1110.0000.0340.0000.0240.0000.0000.1330.0000.0000.0000.1210.0060.0060.0940.0450.0000.0000.1130.0320.0170.1860.1930.0000.0000.0440.0000.0160.0000.0460.0000.0000.0340.2590.0000.0710.1480.0600.1550.0000.0000.0000.0000.0580.0000.0000.0000.0000.0460.0520.0000.0000.0340.0000.0000.0360.0000.0000.0340.0000.0000.0000.0000.0290.0640.064
HouseStyle_SFoyer0.4060.0000.0000.0750.0000.2040.1680.1190.0000.2530.0670.0930.1090.0000.1900.2110.2460.1580.0880.0000.0000.0000.0000.0000.0000.0000.2040.1070.1810.1380.1640.1010.1030.0000.0000.0000.0000.0000.0250.0000.0000.0000.0200.0000.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0120.0000.2040.0740.0000.0000.0000.0000.0110.0000.0000.0670.0000.0170.0000.0000.0000.1200.0000.1670.0560.0000.0410.0000.1540.0000.0000.0991.0000.0000.0000.0410.0000.0450.0000.0000.0000.0000.0000.0000.0000.1050.0230.0000.0000.0570.0000.0000.0540.0390.0090.0000.0000.0000.0000.0000.1060.0310.0000.0000.0000.0340.0000.0000.0510.0360.0000.0000.0000.0000.0000.0000.0000.0870.0910.0000.0000.0490.0000.0300.0380.1060.0740.0000.0000.0000.0310.0000.1010.0650.0000.0000.0000.0000.2280.0880.0290.2060.0000.0000.1120.0000.0350.0960.0000.0000.0440.0210.0000.0130.0000.0000.0000.0000.0000.0000.0580.0000.0000.0000.0580.0210.0210.0210.0000.0000.0000.0330.0000.0000.0500.0630.0000.0000.0000.0000.0000.0000.0000.0000.0620.0290.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0390.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0200.1030.0000.0000.0000.0000.000
HouseStyle_SLvl0.7290.0150.0000.0930.0180.1940.1720.0000.0720.1050.1800.0000.0810.0000.0870.0810.0790.1470.0840.0500.0000.0000.1120.0000.1270.0510.0520.1180.0370.0270.0150.0430.0790.0000.0000.0280.0000.0790.0520.0000.0180.0000.0000.0000.0170.0370.0000.0190.0000.0000.0000.0240.0150.0230.0180.0000.0350.0000.0000.0340.0930.0000.0420.0050.0350.0500.0000.0000.0000.0390.0000.0270.0290.0380.0000.0000.0000.0180.0640.0000.2100.0000.0000.1370.0001.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0950.0000.0000.0810.0000.0000.0520.0000.0000.0000.0060.0000.0000.0000.0000.1330.0000.0270.0000.0240.0000.0000.0490.0000.0000.0000.1400.0910.0420.0190.0000.0670.0860.0000.0000.0000.0000.0000.0610.1200.0640.0000.0000.0000.0390.0000.0000.0270.0120.0000.0000.0000.1900.1180.0500.1720.0960.0220.0250.0000.0100.0410.0730.0000.0000.0000.0480.0450.0000.0000.0000.0000.0000.0000.0720.0000.0450.0000.0390.0430.0430.0250.0000.0000.0000.0000.0000.0000.0440.0740.0000.0000.0000.0000.0000.0000.0000.0000.0000.1640.1030.0000.0760.0000.0580.0000.0000.0160.0140.0000.0370.0000.0000.0390.0000.0110.0000.0000.0000.0000.0000.0000.0530.0000.0300.0290.0000.0000.0000.0000.0540.0290.029
RoofStyle_Flat0.0090.1850.2280.0000.0000.0850.0670.0000.2770.0620.0000.0310.0000.0000.0270.0990.0620.0840.0510.1030.0840.0000.1130.1320.2710.0500.0540.1900.0000.0230.2730.0710.0290.0000.0000.0000.0000.0300.0140.0000.0000.1950.0970.0000.1360.0000.0000.0390.0000.0000.0000.0000.2780.0000.0000.0380.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0191.0000.1680.0000.0270.0000.0000.0000.0000.0910.0000.0000.0000.0150.0000.0140.1520.0000.0000.0570.0000.0000.0000.0000.0380.0000.0000.0000.0100.0000.0120.0000.1760.0000.0000.0550.0000.0000.0040.0290.0320.0000.0000.0000.0110.0260.0000.0000.0470.0000.0340.0000.0500.0400.0000.0000.0000.0000.0000.0000.0000.0000.1000.0000.0720.0000.2080.0000.1040.0000.0000.0000.0550.0000.0270.0820.0000.0100.0000.0290.0730.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0040.1340.1010.0000.0000.0000.0000.0290.0190.0000.0000.0170.0000.0000.0100.0000.0000.0000.0000.0290.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0590.0180.0000.0320.0000.0670.067
RoofStyle_Gable0.2070.1580.0970.2590.0000.1630.1150.2050.1100.1910.2730.3210.1980.0000.1420.0340.1540.1460.1280.0850.0580.0710.0460.1210.0550.0000.0960.0000.0800.0360.0410.1390.1750.0000.0040.0730.0000.1110.0800.0000.0320.0100.0190.0000.0470.0000.0000.0230.0000.0000.0400.0640.0310.0910.0150.0000.0740.0000.0420.0000.0650.0170.0420.0490.1210.0000.0430.0400.0560.0600.0440.0700.0000.0610.0000.0230.0620.0080.1280.0350.1770.0000.0000.0810.0410.0000.1681.0000.1530.9310.1160.0400.0050.0000.0000.0740.0000.0000.0000.0000.0000.0000.0000.0000.0570.0240.0000.0050.0620.0000.0990.0000.0000.0230.0040.0000.0000.0000.0000.0000.0520.0000.0130.0250.1390.1850.0750.2320.0320.0290.0540.0160.0000.0000.0000.0000.0870.0760.0000.0000.0000.0000.2270.0000.1610.0290.0000.0000.0000.0000.0000.1130.0000.0520.0000.0000.0020.0000.0340.0700.0000.0000.0000.0000.0720.0200.0000.0000.0000.0000.0000.0000.0180.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0140.1860.0000.0750.0000.0000.0000.0480.0190.0000.0000.0260.0000.0670.0000.0000.0000.0630.1200.0180.0420.0000.0380.0000.0000.0610.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0460.0000.0250.0250.0000.0000.0000.0000.0480.0000.000
RoofStyle_Gambrel0.0830.0000.0000.0000.1380.1660.0000.0000.0000.0000.0000.0670.1340.0000.0000.0400.0000.2180.0640.0000.0000.0810.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0630.0000.0310.0000.0120.0330.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0340.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0230.0000.0740.0000.0000.0470.0000.0000.0000.1531.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0620.0000.0000.0790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.0000.0180.0400.0000.0000.0000.0000.0080.0790.0700.0000.0000.0720.0860.0000.0000.0000.0000.0000.0000.0000.0430.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0380.0000.0000.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0510.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0940.0000.0000.0000.0970.0300.0390.0750.0000.0000.0000.0000.0000.0000.0580.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0660.0000.0000.0000.0000.0320.032
RoofStyle_Hip0.2330.1500.0390.2860.0000.1780.1330.2260.0740.2090.2870.3330.2200.0000.1340.0590.1580.1580.1550.0770.0710.0670.0630.0740.0360.0000.1140.0000.0970.0550.0220.1300.2080.0000.0000.0640.0070.1160.0840.0000.0360.0110.0000.0000.0120.0000.0000.0000.0000.0000.0350.0740.0000.0740.0000.0000.0630.0000.0370.0000.0640.0090.0320.0620.1400.0340.0370.0490.0460.0470.0550.0830.0000.0520.0000.0460.0550.0000.1360.0300.2060.0000.0000.1000.0450.0000.0270.9310.0201.0000.0000.0000.0000.0000.0000.0680.0000.0000.0280.0000.0000.0350.0000.0000.0220.0000.0210.0000.0000.0000.0980.0000.0000.0470.0190.0000.0000.0000.0000.0000.0210.0000.0180.0050.1640.2190.0970.2530.0410.0140.0740.0000.0000.0130.0000.0210.1070.0620.0000.0000.0000.0000.2540.0270.1610.0130.0000.0000.0000.0000.0000.0680.0000.0360.0000.0000.0310.0000.0430.0860.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0380.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.2030.0000.0740.0000.0000.0000.0130.0250.0000.0000.0000.0000.0920.0000.0000.0000.0790.1370.0000.0730.0000.0400.0000.0000.0740.0000.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.0670.0000.0540.0250.0000.0000.0000.0000.0630.0000.000
RoofStyle_Mansard0.0360.0610.0000.0000.0000.2470.0000.0000.0760.0000.0000.0000.0440.0000.0500.0660.0660.0450.0000.0430.0000.3720.0000.0620.0000.0000.0370.0000.0000.2150.0000.0000.1620.0560.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0940.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0440.0000.0000.0000.1160.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.1000.0000.1030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0450.0000.0680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
RoofStyle_Shed0.0810.0000.1250.0000.0000.0380.1090.0120.0720.0000.0000.0000.0880.0000.0470.2820.2080.0000.0280.0180.0000.0400.0000.0000.0000.0110.1750.0000.2300.2000.0680.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0570.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0920.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0770.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Exterior1st_AsbShng0.1120.0000.0000.2870.1460.1620.1800.0320.0000.0000.0710.0340.1300.0000.0000.0240.0000.2180.1020.0480.0000.1080.0000.0000.0000.0000.0000.0000.0810.0410.1730.0630.1110.0100.0000.0000.0000.1020.1210.0000.0000.0000.0150.0000.0000.0000.0000.0080.0000.0000.0000.0770.0000.0150.0000.0030.0000.0700.0000.0000.0000.0000.0000.0000.0000.0830.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0800.0000.0450.0000.0880.0000.0000.0000.0000.0050.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0320.0000.0320.0000.0000.0000.0770.0290.0000.8220.0000.0000.0000.0000.0000.0300.0000.0310.0000.0000.0000.0000.0750.0270.0000.0000.0530.0630.0000.0000.0000.0730.0690.0000.0400.0000.0000.0000.0840.0000.0450.0000.0280.0000.0000.0290.0790.0610.0590.0000.0000.0400.0000.0080.0000.0000.0320.0120.0630.0000.0000.0540.0000.0000.0000.0000.0000.0000.0000.0790.0000.0220.0710.0000.0740.1230.0440.0000.0000.2180.2180.0980.0000.1880.0000.1180.0000.0670.0610.0450.0000.0000.0000.0000.0000.0000.0000.0280.1100.0590.0000.0000.0340.0530.0340.0430.0000.0900.0000.0540.1430.0000.1130.0000.1170.1750.0180.0000.0000.0000.0000.0350.0000.0000.0000.0420.0430.0000.0000.0000.0000.0530.053
Exterior1st_AsphShn0.1170.0000.0000.0000.0000.0000.0680.0000.0000.0000.0580.0360.0000.0000.0000.0000.0000.0000.0740.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1130.0000.0950.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2870.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0380.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Exterior1st_BrkComm0.0000.0000.0000.1370.2710.0000.0350.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0960.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0910.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3990.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0570.0000.0000.0000.0000.0000.0000.0630.0000.0000.0000.0000.0000.0000.0390.0000.0000.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0510.0750.0000.0000.0000.0000.0360.0000.0000.0000.0090.0090.0100.0580.0000.0000.0840.0000.0440.0000.0000.0000.0000.0530.0000.0840.0000.0960.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.000
Exterior1st_BrkFace0.0800.0540.1250.0000.0330.1790.1850.0000.1330.0000.1060.1420.0390.0000.0420.0000.0870.1720.0840.0390.0710.0450.1650.1480.0000.0520.0540.0000.0000.0000.0000.1380.0610.0000.0000.0180.0000.0270.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0510.0940.0120.0260.0000.0000.0000.1420.0000.0000.0000.0250.0000.0000.0230.0130.0290.0000.0000.0890.0000.0000.0000.0000.0000.0080.0000.0760.0000.0000.0840.0000.0000.0000.0740.0000.0680.0000.0000.0000.0000.0001.0000.0000.0140.0700.0000.0690.0380.0000.0000.1330.0660.0000.0000.0000.0000.6570.0000.0140.0290.0000.0680.0000.0000.0820.0000.1300.0450.0000.0000.1180.1430.0280.0000.0000.0210.0430.0000.0000.0000.0000.0000.0000.0440.0840.0750.0000.0000.0420.0000.0440.0570.0000.0000.0000.0140.0460.0000.0000.0000.0170.0340.0770.0440.0000.0440.0000.0000.0000.0000.0460.0770.0000.0840.0590.0000.0000.0950.0700.0760.0200.0000.0000.0000.0000.1090.0000.0000.0000.1070.0000.0000.0430.0490.0300.0000.0000.0000.0260.0000.0380.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0430.0000.0370.0000.0000.0000.0000.0470.0440.0000.000
Exterior1st_CBlock0.0280.0000.0000.0420.0000.0370.0000.0000.0000.0000.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.1290.0000.0000.0000.0660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4990.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0590.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Exterior1st_CemntBd0.1850.1650.0000.2040.0120.1610.1890.1940.0000.0910.1080.1800.1320.0000.1410.0590.1880.1390.1310.1130.1250.0000.0000.0430.0000.0000.0590.0000.0850.0000.0090.0000.1250.0000.0000.0750.0000.1540.1360.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0080.0000.0000.0170.0000.0000.0330.0000.5030.0000.0700.0000.0000.0000.0430.0320.0000.0000.0220.0970.1420.0000.0000.1410.0000.0160.1330.1470.0350.0000.0020.0000.0000.0250.1050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0001.0000.0790.0000.0790.0450.0000.0000.1480.0750.0000.0000.0000.0000.0000.0000.9660.0760.0000.0770.0000.0570.0000.0000.1460.0620.0000.0000.0370.0290.1320.2260.0000.0000.0910.0000.0000.0000.0000.0160.0460.0090.0600.0000.0000.0000.1270.0000.0190.0830.0080.0000.0000.0470.0310.0000.0000.0280.0000.0160.0950.0000.0250.0000.0000.0000.0190.0000.0000.0140.0000.0000.0000.0000.0000.0000.0520.0000.0200.0000.0000.0220.0220.0400.0000.0000.0000.0380.2200.0000.0090.0720.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0480.0000.0950.1070.0170.0850.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.1260.0000.0810.0000.0000.0000.0000.0520.1230.0320.032
Exterior1st_HdBoard0.1560.0000.0000.1510.0760.4500.4120.1040.0820.1600.0430.0320.0910.0000.1160.1020.0990.3370.0970.0430.0000.0500.0740.0000.0430.0000.0330.0350.1100.0780.0000.0000.1210.0000.0000.0830.0000.1350.0850.0400.0000.0000.0000.0000.1070.0390.0000.0870.0260.0000.2010.0560.0000.0520.0480.0430.0000.0560.0260.1140.0280.0000.1410.0270.0920.0940.0410.1830.0890.0990.0480.0000.0000.0000.0000.0000.0210.0320.1150.0180.0000.0000.0000.0000.0230.0950.0150.0000.0000.0280.0000.0000.0320.0000.0000.0700.0000.0791.0000.0000.1740.1130.0000.0410.3100.1670.0420.0320.0000.0000.0410.0000.0790.8800.0000.1650.0000.0000.0000.0420.3040.1570.0440.0000.1710.1090.0760.0610.0180.1260.1580.0000.0000.0000.0000.0190.1290.2690.1820.0000.0000.0000.0930.0000.0310.1080.0260.0000.0000.0000.0000.0190.0000.0000.1580.0710.0140.0000.0000.1310.0000.0000.0000.0890.0200.0580.0000.0350.0000.0000.0000.0000.2200.0170.0490.0000.2100.0810.0810.0710.0260.0000.0000.0890.1000.0450.0680.1420.0180.0000.0000.0000.0210.0000.0000.0000.0940.0000.0660.0000.0360.0570.0000.0450.0000.0440.0060.0000.0510.0000.0240.0720.0000.0290.0000.0000.0000.0000.0000.0000.1220.0000.0790.0000.0000.0000.0000.0980.1230.0630.063
Exterior1st_ImStucc0.0000.0000.0000.0000.0000.0190.0000.0680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0510.0000.0440.1290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Exterior1st_MetalSd0.2080.1070.0000.1990.1560.2430.1800.0490.0000.1020.1170.1270.1560.1170.1100.1010.1390.2310.1380.1170.0700.1410.0000.0000.0000.0160.0000.0070.1690.0710.0000.1280.2040.0740.0640.1150.0500.1570.0690.0610.0000.0000.0440.0000.0710.0000.0000.0490.0260.0560.0230.0710.0000.1180.0130.0500.0750.0670.0260.0450.1170.0000.0410.0170.0050.1080.0000.0180.0670.0730.0210.0060.0000.0910.0730.0090.0220.0280.1240.0820.0330.0000.0310.0450.0000.0000.0140.0000.0000.0000.0000.0000.0320.0000.0000.0690.0000.0790.1740.0001.0000.1120.0000.0410.3080.1660.0420.0320.0000.0000.0410.0000.0780.1500.0000.9700.0000.1330.0000.0230.3030.1500.0440.0000.0240.0910.0900.0000.0000.1560.1520.0000.0000.1090.0000.1000.0950.1010.1600.0000.0000.0000.0470.0600.1710.1820.0000.0520.0000.0000.0970.0440.0000.0920.0150.0520.0870.0000.1000.0000.0000.0000.0000.0000.0060.0120.0000.0000.0000.0000.0000.0000.0730.0000.0180.0000.0380.0190.0190.0510.0000.0000.0000.0520.0420.0170.1290.1420.0000.0510.0000.0000.0210.0000.0000.0400.1920.0000.0650.0000.2380.1070.0940.1740.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0220.0000.0000.0100.0000.0000.0780.0360.0180.0300.0000.0090.0000.0300.0800.0920.092
Exterior1st_Plywood0.1810.0000.1330.1260.0760.4220.4420.0930.1180.0610.0140.0980.0610.0000.0120.0900.0340.3590.1680.1080.0420.0000.0000.1060.1290.0110.1540.0310.1250.0830.0800.1270.1620.0280.0000.0480.0000.1280.1120.0000.0000.0950.0000.0000.0270.0000.0000.0000.0000.0000.0000.0440.1200.0780.0000.0000.0430.0000.0000.0500.0000.2610.1790.0300.0550.0620.0070.0400.1320.0600.0070.0000.1080.0930.0000.1340.0000.0300.0530.0000.0240.0000.0000.0400.0570.0810.1520.0000.0000.0350.0000.0920.0000.0000.0000.0380.0000.0450.1130.0000.1121.0000.0000.0070.2040.1080.0100.0000.0000.1490.0070.0000.0450.0930.0490.1100.0000.7500.0000.0100.2010.0890.0280.0000.0480.0000.0490.0390.0000.1110.1290.0000.0000.0000.0000.0000.0860.2130.1630.0240.0000.0000.0760.0240.0620.0000.0090.0000.0000.0000.0000.1200.0000.0480.1380.0000.0260.0000.0000.0960.0000.0000.0000.0880.0410.1210.0000.0210.0000.0000.0000.0000.1670.0000.0000.0000.1870.0520.0520.0520.0130.0000.0000.0680.0310.0000.0750.0970.0000.0000.0300.0000.0000.0340.0000.0000.0930.0000.0480.0100.0670.0380.0160.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0160.0250.0000.0000.0000.0000.0000.0760.0000.0450.0000.0000.1310.0630.0000.0780.0590.059
Exterior1st_Stone0.0000.0000.0000.0000.0000.0000.0000.0120.1760.0000.0000.3500.0000.0000.0470.0000.0660.0000.0000.0000.0000.2600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0000.0000.0000.0000.0000.0880.0380.0000.0600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0960.0000.0000.0000.0480.0000.0140.014
Exterior1st_Stucco0.0350.1670.0190.1540.1280.2510.1170.1980.0000.0400.1970.1900.0720.0000.2190.0390.0200.1470.1630.0000.0110.3170.0000.0000.2760.0000.0190.0000.0340.0000.0000.0730.0830.0400.0000.0000.0000.0110.0490.0260.0400.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0710.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.1070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0410.0000.0410.0070.0001.0000.0880.0380.0000.0000.0000.0000.0000.0000.0000.0380.0000.0390.0000.0220.0270.7600.0860.0360.0550.0000.0390.0430.0000.0000.0000.0480.0350.0000.1130.0240.0000.0840.1560.0510.0410.0000.0200.0000.0000.0600.0930.0510.0000.0000.0000.0220.0410.0000.0000.0000.0210.0000.0480.0140.0920.0000.0000.0000.0000.0000.0000.0000.0000.1030.1020.0000.0000.0350.0360.0410.0000.0000.0000.1010.1010.0310.1160.0000.0000.0970.0000.0080.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.1090.0000.0000.0000.1150.0170.0610.0830.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Exterior1st_VinylSd0.2990.0670.0670.4010.4060.6880.5440.0000.1180.2600.1970.1030.2260.0510.2220.1190.2030.6360.2750.2400.2050.1760.0000.1070.0000.0080.0570.0380.3460.1020.0700.1270.3630.0000.0460.0690.0000.0710.1070.1220.0000.0710.1060.0420.0000.0180.0000.0000.1240.0000.0660.1070.0840.3510.1180.0800.2120.0730.0690.0000.1960.0410.1040.0650.2120.1120.0260.0850.0000.1260.0000.0780.0500.0520.0470.0800.0170.0110.1460.0600.0000.0000.0500.1680.0540.0520.0570.0570.0000.0220.0000.0000.0770.0000.0000.1330.0000.1480.3100.0000.3080.2040.0000.0881.0000.2960.0900.0630.0000.0310.0880.0000.1470.2930.0240.3030.0000.2290.0170.0790.9760.2840.0660.0300.0550.1860.2280.0300.0000.4650.4660.0000.0500.0770.0000.0990.1850.4280.5600.0360.0250.0000.2000.0690.3550.4220.0460.0540.0000.0000.1600.0000.0000.0950.1340.1480.2690.0850.1590.1060.0000.0420.0000.0930.0760.1570.0000.0820.0710.0000.0000.0000.4560.0980.0990.0000.3720.1310.1310.1240.0700.0000.0000.1510.0880.0490.3640.3860.0000.0000.0900.0560.0000.0000.1030.0000.1870.0310.1820.0410.2460.2340.1960.3540.0000.0880.0430.0000.1300.0000.0370.0410.0000.1000.0870.0000.0000.0000.0000.0000.2760.0000.1650.0440.0000.0350.0180.1360.2780.0850.085
Exterior1st_Wd Sdng0.2170.1260.0370.1890.2410.4340.2650.0660.0630.1130.1040.0290.1010.1040.0660.1230.0210.3440.1690.1160.1400.1580.0000.1280.0000.0000.0360.0000.1950.1110.0000.0510.1950.0330.0000.0780.0000.0770.1280.1490.0280.0000.0840.0460.0470.0230.0000.0000.0230.0000.0200.1520.0750.1310.0960.0600.0710.0870.0230.0650.0580.0000.0650.0070.0870.1420.1340.0000.0000.0940.0380.0400.0000.1100.0500.0310.0590.1110.1760.0660.0000.0860.0620.1050.0390.0000.0000.0240.0620.0000.0000.0000.0290.0000.0000.0660.0000.0750.1670.0000.1660.1080.0000.0380.2961.0000.0390.0000.0000.0000.0160.0000.0750.1480.0000.1580.0000.0720.0000.0190.2830.8560.0290.0380.1050.1560.1120.0560.0440.1660.1750.0000.0260.0000.0070.0430.2740.0300.2040.0000.0000.0000.0860.0800.2080.2360.1130.0000.0000.0330.0880.0000.0300.0560.0250.0000.1650.0670.0760.0910.0000.0000.0000.0000.0440.0000.0000.0300.0000.0670.0000.0000.1070.0430.0220.0000.0650.1660.1660.1360.0790.0000.0000.1640.0530.0680.1270.1300.0000.0000.0340.0560.0070.0000.0850.0000.1590.0180.0600.0160.1670.1500.1190.2150.0390.1150.0160.0000.1440.0000.0530.0160.0000.1030.0110.0000.0000.0000.0000.0000.1160.0000.0820.0000.0000.0000.0000.0580.1100.0640.064
Exterior1st_WdShing0.0630.0000.0000.0290.0340.0980.0730.0460.0000.0000.0310.0000.0000.0000.0730.0740.0110.0580.0410.1860.0000.0000.0000.0000.0000.1110.0000.1300.0480.0170.1910.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0720.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0580.0000.0000.0000.0000.0280.0090.0000.0000.0000.0000.0210.1000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0420.0100.0000.0000.0900.0391.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0220.0000.0000.0890.0160.5140.0000.0280.0350.0000.0000.0000.0380.0390.0000.0000.0000.0000.0000.0200.0210.0680.0000.0180.0000.0000.0000.0630.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0410.0000.0000.0000.0620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0240.0000.0460.0000.0000.0000.0160.0000.0510.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0190.019
Exterior2nd_AsbShng0.1170.0000.0000.2850.1420.1740.1970.0000.0000.0780.0140.0000.0980.0000.0150.0240.0990.2200.0880.0490.0000.1100.0000.0000.0000.0280.0240.0000.0810.0410.1610.0480.0950.0000.0000.0000.0000.1160.1210.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0150.0000.0000.0000.0700.0000.0000.0000.0000.0000.0000.0000.0830.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0800.0000.0450.0000.0880.0000.0000.0000.0000.0050.0000.0000.0000.0000.8220.0000.0000.0000.0000.0000.0320.0000.0320.0000.0000.0000.0630.0000.0001.0000.0000.0000.0000.0000.0000.0300.0000.0310.0000.0120.0000.0000.0750.0270.0000.0000.0530.0630.0000.0000.0750.0590.0420.0000.0870.0000.0000.0000.0640.0000.0580.0000.0280.0000.0000.0000.0790.0860.0590.0000.0000.0110.0000.0080.0000.0150.0320.0410.0630.0000.0000.0540.0000.0000.0000.0000.0000.0000.0000.0790.0000.0220.0710.0000.0740.0890.0610.0000.0000.1700.1700.0980.0000.0540.0000.0970.0000.0240.0610.0580.0000.0000.0000.0000.0000.0000.0000.0000.1100.0590.0000.0110.0500.0380.0340.0430.0000.0550.0000.0540.1030.0000.0730.0000.1170.1340.0180.0000.0000.0000.0000.0350.0000.0000.0000.0420.0000.0000.0000.0000.0000.0660.066
Exterior2nd_AsphShn0.0640.0000.0000.0000.0000.0000.0910.0000.0350.0000.0720.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1160.0000.1610.0450.0000.0250.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0620.0790.0000.1030.0000.0000.2870.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0390.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Exterior2nd_Brk Cmn0.2200.0960.0000.0780.1360.0830.1240.0000.0000.0000.0000.0000.0990.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0460.0000.0000.0000.0340.0000.0000.0000.0660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.5640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0860.0000.0000.1320.0240.0000.0000.0000.0000.0000.0440.0000.0000.0380.0000.0000.0000.0000.0000.0000.0000.3990.0000.0000.0000.0000.0000.0000.1490.0000.0000.0310.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0240.0400.0000.0000.0000.0280.0350.0000.0040.0000.0000.0000.0000.0420.0450.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0920.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0550.0650.0000.0000.0460.0000.0000.0000.0080.0000.0000.0180.0000.0000.0040.0000.0000.0000.0000.0000.0330.0000.0300.0000.0000.0000.0000.0000.0000.0090.0210.0680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.033
Exterior2nd_BrkFace0.0590.0220.1890.0000.0000.0970.1160.0000.1180.0580.1640.1620.0510.0000.0000.0380.0760.0760.0490.0000.0000.0700.1850.1500.0000.0000.0810.0090.0120.0000.0000.0770.0060.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0130.0000.0000.0940.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1390.0000.0000.0000.0000.0000.0000.0000.0810.0000.0000.0530.0000.0000.0000.0990.0000.0980.0000.0000.0000.0000.0000.6570.0000.0000.0410.0000.0410.0070.0000.0000.0880.0160.0000.0000.0000.0001.0000.0000.0000.0380.0000.0390.0000.0220.0000.0000.0860.0360.0000.0000.0770.1010.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0280.0000.0000.0000.0150.0000.0000.0250.0000.0000.0000.0000.0210.0000.0000.0000.0310.0000.0340.0000.0000.0000.0000.0000.0000.0000.0350.0160.0000.0220.0000.0000.0000.0350.0000.0000.0400.0000.0000.0000.0000.0560.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0610.0000.0000.0000.0150.0000.0100.0000.0000.0000.0000.0480.0170.0000.000
Exterior2nd_CBlock0.0280.0000.0000.0420.0000.0370.0000.0000.0000.0000.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.1290.0000.0000.0000.0660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4990.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0590.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Exterior2nd_CmentBd0.1870.1620.0000.2120.0280.1660.1990.1870.0000.0930.1210.1830.1130.0000.1330.0600.1990.1450.1330.1210.1300.0000.0000.0000.0000.0000.0460.0000.0700.0000.0060.0000.1280.0050.0000.0760.0000.1570.1380.0000.0000.0000.0000.0000.0200.0000.0000.0310.0000.0000.0100.0000.0000.0150.0030.0000.0330.0000.5080.0000.0690.0000.0000.0000.0440.0310.0000.0300.0210.0990.1160.0000.0000.1430.0000.0160.1350.1500.0340.0000.0000.0000.0000.0180.1060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.9660.0790.0000.0780.0450.0000.0000.1470.0750.0000.0000.0000.0000.0000.0001.0000.0750.0000.0770.0000.0560.0000.0000.1440.0720.0000.0000.0440.0230.1350.2290.0000.0000.0940.0000.0000.0000.0000.0140.0450.0180.0640.0000.0000.0000.1420.0000.0120.0880.0060.0000.0000.0460.0340.0000.0000.0320.0000.0140.0980.0000.0400.0000.0000.0000.0200.0000.0000.0120.0000.0000.0000.0000.0000.0000.0570.0000.0310.0000.0000.0210.0210.0390.0000.0000.0000.0370.2220.0000.0000.0760.0000.0000.0000.0000.0000.0000.0240.0000.0480.0000.0330.0000.1010.1100.0130.0900.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1280.0000.0830.0000.0000.0000.0000.0550.1250.0290.029
Exterior2nd_HdBoard0.1580.0000.0000.1250.0560.4200.3690.0970.0670.1400.0000.0000.0770.0000.1030.0830.0610.3170.1000.0410.0000.0230.0610.0000.0460.0000.0320.0460.0700.0880.0000.0000.1170.0200.0000.0790.0000.1290.0810.0460.0000.0150.0000.0000.0850.0470.0000.0680.0230.0000.1910.0510.0000.0580.0300.0450.0000.0530.0230.0550.0450.0000.1520.0350.0880.0880.0380.1410.0770.0940.0540.0000.0000.0000.0000.0000.0100.0220.1010.0140.0000.0000.0000.0000.0310.1330.0100.0230.0000.0470.0000.0000.0300.0000.0000.0290.0000.0760.8800.0000.1500.0930.0000.0380.2930.1480.0000.0300.0000.0000.0380.0000.0751.0000.0000.1640.0000.1270.0000.0390.2920.1560.0540.0050.1660.1130.0690.0560.0140.1080.1390.0000.0000.0000.0000.0000.1230.2390.1570.0000.0000.0000.0860.0000.0000.0720.0200.0000.0000.0000.0000.0000.0000.0000.1080.0490.0000.0000.0180.1200.0000.0000.0000.0970.0300.0660.0000.0310.0000.0000.0000.0000.2010.0050.0600.0000.1790.0660.0660.0660.0220.0000.0000.0820.0870.0550.0520.1240.0000.0000.0330.0400.0180.0000.0000.0000.0930.0000.0600.0000.0430.0220.0270.0000.0000.0250.0430.0000.0340.0000.0000.0770.0000.0160.0100.0000.0000.0000.0050.0000.1160.0000.0640.0000.0000.0000.0000.0860.1180.0440.044
Exterior2nd_ImStucc0.0000.0930.0000.0330.0000.0800.0520.1150.0000.0780.0540.0390.2130.0000.2080.0000.0000.0190.0860.0000.0000.1170.0000.0000.1720.0290.0000.0000.0000.0000.0000.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0960.0000.0000.0000.1080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.1540.0000.0490.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0700.0000.0240.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.000
Exterior2nd_MetalSd0.2140.1110.0000.1890.1420.2380.1790.0420.0000.0980.1140.1250.1660.1200.1060.0990.1350.2370.1430.1280.0620.1330.0000.0000.0000.0000.0000.0220.1660.0730.0000.1210.2050.0740.0660.1190.0530.1600.0690.0740.0000.0000.0490.0000.0680.0000.0000.0560.0250.0580.0220.0750.0000.1150.0180.0360.0730.0700.0250.0550.1120.0000.0380.0130.0000.1050.0000.0140.0650.0770.0190.0000.0000.0920.0760.0000.0260.0330.1360.0840.0280.0000.0330.0510.0000.0270.0120.0000.0000.0000.0000.0000.0310.0000.0000.0680.0000.0770.1650.0000.9700.1100.0000.0390.3030.1580.0410.0310.0000.0000.0390.0000.0770.1640.0001.0000.0000.1300.0000.0410.2980.1590.0560.0000.0310.0940.0870.0000.0000.1500.1440.0000.0000.1070.0000.1000.0870.0960.1510.0000.0000.0000.0440.0340.1630.1790.0000.0500.0000.0090.1040.0480.0080.0980.0250.0640.0810.0000.0910.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0610.0000.0070.0000.0270.0000.0000.0460.0000.0000.0000.0490.0390.0210.1210.1320.0000.0000.0000.0000.0190.0000.0000.0420.1840.0000.0800.0000.2430.1010.0960.1780.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0130.0000.0000.0750.0370.0080.0350.0000.0070.0000.0230.0780.0840.084
Exterior2nd_Other0.0000.0000.0000.0000.0000.0000.0000.0680.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0510.0000.0000.0000.0000.1920.0000.0370.0000.0000.0000.0000.0000.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0940.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0110.0000.0000.0000.0000.0000.0290.0000.000
Exterior2nd_Plywood0.1770.0520.1260.1360.0980.3980.3920.0770.1520.0750.0000.0960.1120.0000.0000.0780.0840.3610.1420.1150.0310.0320.0790.1000.1070.0260.1280.0410.0820.0760.1090.1070.1320.0000.0000.0600.0000.1540.1320.0000.0000.0700.0000.0000.0740.0070.0000.0230.0000.0000.0000.0000.1280.0800.0000.0000.0580.0160.0000.0690.0000.0730.1610.0410.0670.0780.0000.1160.1120.0730.0220.0000.0880.0000.0000.1280.0430.0000.0500.0000.0930.0000.0000.0850.0340.0240.1760.0000.0000.0000.0000.0770.0000.0000.0000.0000.0000.0570.0000.0000.1330.7500.0000.0220.2290.0720.0220.0120.0000.0000.0220.0000.0560.1270.0000.1300.0001.0000.0000.0240.2350.1240.0380.0000.0250.0000.0500.0500.0000.1450.1670.0000.0000.0000.0240.0000.0780.2540.2050.0000.0000.0000.0910.0120.0000.0140.0000.0000.0000.0000.0000.0880.0000.0300.1470.0970.0730.0000.0090.1100.0210.0240.0000.0750.0300.1220.0000.0320.0010.0000.0000.0000.1860.0000.0000.0000.2160.0580.0580.0670.0250.0000.0000.0760.0610.0000.1080.1320.0000.0000.0300.0000.0390.0240.0350.0000.0850.0000.0630.0000.0750.0250.0000.0170.0000.0100.0000.0000.0000.0000.0120.0000.0000.0000.0180.0000.0000.0000.0000.0000.0910.0000.0650.0000.0420.1080.0440.0000.0930.1000.100
Exterior2nd_Stone0.0990.0000.0550.0840.0680.0510.0310.0000.0260.0000.0610.2140.0000.0000.0000.0000.0750.0410.0000.0000.0000.1510.0000.0000.0000.0470.0000.0000.0540.0000.1510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0820.0000.0000.0000.0000.0000.0000.1540.0270.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.0000.0280.0000.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0550.0000.0170.0000.0000.0000.1060.0000.0000.0000.0000.0000.0490.0490.0000.1200.0000.0000.0360.0000.0000.0250.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0540.0000.0000.0000.0000.000
Exterior2nd_Stucco0.0460.1680.0120.0970.1440.2530.1410.1920.0000.0000.1910.1840.0920.0000.2200.0710.0000.1970.1490.0000.0500.3500.0000.0000.2700.0000.0000.0000.0410.0000.0000.0770.0790.0000.0000.0000.0000.0430.0620.0550.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.1270.0720.0000.0000.0000.0000.0000.0000.0000.0000.0000.1230.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0140.0380.0000.0240.0000.0000.0120.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0420.0000.0230.0100.0000.7600.0790.0190.0000.0000.0000.0000.0000.0000.0000.0390.0000.0410.0000.0240.0001.0000.0890.0370.0000.0000.0420.0470.0000.0000.0000.0510.0510.0000.0710.0000.0000.0260.1860.0660.0450.0000.0180.0000.0000.0580.0850.0670.0440.0000.0000.0000.0420.0000.0000.0540.0000.0000.0500.0440.0890.0000.0000.0000.0000.0000.0000.0000.0000.2080.2420.0000.0000.0330.0530.0710.0000.0000.0200.1620.1620.0540.0730.0000.0000.0930.0000.0000.0170.0000.0000.0250.0000.0160.0000.0000.0240.0180.1350.0000.0000.0000.1340.0510.0760.1210.0000.0400.0000.0000.0410.0000.0580.0000.0000.0270.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Exterior2nd_VinylSd0.3130.0760.0640.4010.3980.6820.5450.0000.1330.2610.1970.1080.2180.0490.2130.1160.2050.6320.2770.2410.2140.1800.0000.1120.0000.0260.0540.0330.3350.1090.0810.1270.3620.0190.0440.0730.0100.0700.1050.1190.0000.0690.1010.0470.0000.0340.0000.0000.1270.0000.0640.1040.0820.3580.1160.0700.2160.0810.0670.0000.1990.0400.1000.0680.1910.1070.0380.0880.0000.1250.0000.0710.0480.0710.0670.0770.0470.0090.1460.0580.0000.0000.0480.1670.0510.0490.0550.0520.0000.0210.0000.0000.0750.0000.0000.1300.0000.1460.3040.0000.3030.2010.0000.0860.9760.2830.0890.0750.0000.0300.0860.0000.1440.2920.0440.2980.0000.2350.0150.0891.0000.2840.1110.0280.0630.1790.2040.0240.0000.4640.4600.0000.0480.0670.0000.0880.1800.4280.5600.0470.0240.0000.2010.0660.3500.4150.0520.0500.0000.0200.1680.0000.0000.0980.1320.1530.2700.0820.1650.1110.0000.0510.0000.0990.0810.1680.0000.0800.0700.0000.0000.0000.4540.1040.1000.0000.3670.1340.1340.1260.0680.0000.0000.1520.0870.0560.3610.3800.0000.0000.0990.0740.0000.0000.1230.0000.1870.0290.1680.0400.2410.2310.1950.3510.0000.0860.0420.0000.1260.0000.0340.0400.0000.0950.0840.0000.0000.0000.0000.0000.2770.0000.1680.0530.0000.0330.0140.1370.2790.0790.079
Exterior2nd_Wd Sdng0.2340.1320.0000.1860.2300.4130.2970.0840.1010.1020.1100.0570.0920.0870.0630.0880.0000.3390.1610.1300.1740.1190.0000.1340.0000.0000.0510.0000.1980.0900.0000.0680.1990.0120.0000.0760.0000.0680.1210.1560.0000.0000.0920.0390.0530.0350.0000.0000.0210.0000.0180.1270.0480.1280.0550.0750.0670.0790.0210.0630.1030.0000.0520.0410.0850.1420.1230.0000.0240.0910.0360.0370.0000.1040.0540.0280.0570.1080.1860.0910.0000.0600.0000.1220.0360.0000.0000.0000.0390.0000.0000.0000.0270.0000.0000.0450.0000.0620.1570.0000.1500.0890.0000.0360.2840.8560.0160.0270.0000.0000.0360.0000.0720.1560.0000.1590.0000.1240.0000.0370.2841.0000.0520.0130.1080.1650.1090.0540.0200.1740.1850.0000.0000.0000.0000.0210.2710.0450.2240.0000.0000.0000.0900.0850.2090.2280.1060.0180.0000.0410.0990.0000.0160.0620.0130.0000.1750.0920.1050.0580.0000.0320.0000.0000.0360.0510.0000.0350.0000.0690.0000.0000.1240.0600.0190.0000.0780.1500.1500.1930.0670.0000.0000.2090.0490.0600.1430.1470.0000.0000.0540.0600.0130.0000.0920.0000.1530.0220.0560.0000.1720.1520.1050.2140.0410.1210.0200.0000.1330.0000.0420.0000.0000.0750.0180.0000.0000.0000.0000.0000.1130.0000.0760.0000.0000.0000.0000.0840.1070.0780.078
Exterior2nd_Wd Shng0.0000.0040.0850.0000.0280.1340.0590.0200.0000.0000.0310.0860.0460.0000.0000.0580.0000.1620.0710.1490.0480.1400.0000.0000.0000.0640.0000.0150.0300.0480.0000.0000.0490.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0080.0000.0140.0000.0000.0000.0000.0000.0090.0000.0400.0000.0100.0100.0000.0180.0000.0000.0000.0000.0000.0000.0230.0000.0410.0000.0270.0000.0540.0000.0000.0000.0000.0130.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0440.0280.0000.0550.0660.0290.5140.0000.0000.0000.0000.0000.0000.0540.0000.0560.0000.0380.0000.0000.1110.0521.0000.0000.0610.0350.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0620.0000.0270.0140.0000.0000.0000.0000.0510.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0860.0000.0190.0000.0940.0000.0520.0000.0460.0060.0340.0320.0000.0150.0000.0000.0440.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0410.041
MasVnrType_BrkCmn0.0000.0000.1730.0240.0290.1340.1010.0000.1590.0000.0000.0000.0000.0000.0000.0000.0000.1420.0000.0310.0000.0000.0000.0000.0000.0000.0000.0540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0110.0000.0420.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0540.0000.0000.0000.0000.0000.0000.0000.0740.0000.0000.0000.0000.0000.0000.0810.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0040.0250.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0380.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0280.0130.0001.0000.0540.1130.0000.0000.0000.0250.0360.0000.0000.0000.0000.0000.0000.0780.0650.0000.0000.0000.0000.0000.0460.0610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0510.0000.1660.0110.0000.0000.0000.0000.0650.0390.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0620.0000.0000.0490.0000.0000.1240.0610.0000.0000.0000.0000.1190.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0610.0000.0110.0000.0180.0000.0110.011
MasVnrType_BrkFace0.2100.1320.0000.2480.1630.3230.2340.1320.0280.1780.1770.1700.1650.0460.1710.1150.1630.2590.2050.1370.1310.1320.0820.0000.0000.0000.0780.0560.1710.1440.0290.1590.2190.0000.0380.0370.0000.1350.1050.1040.0000.0460.1100.0210.0240.0060.0000.0000.1410.0000.1350.1130.0200.0680.0200.0840.0260.0890.0440.0000.0650.0340.1840.1780.0610.1700.0650.0220.0000.0560.0000.0690.0180.0650.0560.0270.0500.0600.1650.0510.0000.0290.0420.0830.0000.1400.0290.1390.0180.1640.0000.0000.0530.0000.0000.1180.0000.0370.1710.0000.0240.0480.0000.0390.0550.1050.0280.0530.0000.0240.0770.0000.0440.1660.0000.0310.0000.0250.0000.0420.0630.1080.0610.0541.0000.7960.2010.0470.0510.1290.1340.0000.0480.0000.0000.0470.2070.0480.0840.0200.0160.0000.0510.0750.1120.0990.0560.0000.0000.0680.0480.0650.0310.0350.0000.0000.1660.0320.0000.1160.0000.0000.0330.0000.0000.0430.0000.0580.0310.0240.0000.0000.0650.0460.0180.0000.0000.1330.1330.1070.0000.0000.0000.1130.0000.0730.1150.0960.0510.0000.0160.0730.0000.0000.1030.0000.2330.0000.0330.0340.2230.1160.1580.1940.0000.1060.0000.0000.1650.0000.0850.0000.0240.1490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0250.0000.0240.024
MasVnrType_None0.2600.2190.0000.3990.2670.4380.3030.2260.0540.2470.3470.3160.2040.0810.2500.0940.2380.3690.3530.1980.1740.1870.0670.0000.0000.0360.1420.0370.2810.1080.0500.2240.3850.0000.0550.0000.0310.1540.1480.1080.0000.0270.0850.0240.0230.0000.0000.0000.1080.0000.1030.1420.0140.0600.0310.0800.0610.1180.0660.0230.0270.0500.1430.1300.2740.2100.0900.0410.0000.0560.0000.0830.0400.0730.0740.0000.0950.0730.2180.0700.0590.0450.0590.0740.0000.0910.0320.1850.0400.2190.0000.0000.0630.0000.0000.1430.0000.0290.1090.0000.0910.0000.0000.0430.1860.1560.0350.0630.0000.0400.1010.0000.0230.1130.0000.0940.0000.0000.0000.0470.1790.1650.0350.1130.7961.0000.3700.1960.0700.2510.3040.0000.0650.0700.0000.1040.2590.0270.2210.0520.0330.0000.2470.0950.1310.2100.0840.0000.0000.0950.1030.1270.0000.1160.0400.0180.2600.0630.0000.1200.0000.0100.0540.0000.0000.0880.0000.0870.0550.0400.0000.0000.1930.0690.0900.0000.1010.1810.1810.1450.0390.0000.0000.1610.1670.1040.1780.2260.0000.0000.0160.0830.0250.0000.0860.0000.2920.0000.0930.0000.2970.2060.1990.2890.0000.1390.0000.0000.2260.0000.1040.0000.0400.1970.0000.0000.0000.0000.0000.0000.1680.0000.1280.0260.0000.0000.0000.0890.1650.0800.080
MasVnrType_Stone0.1510.1650.0610.3720.1960.3520.4050.2370.0000.2120.3040.2920.0750.0000.1820.0000.1740.3890.3320.1620.1140.0700.0000.0320.0220.0410.1100.0000.2070.0460.0060.1250.3440.0600.0000.0500.0000.0290.0600.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.0400.0330.0000.0000.0630.0000.0190.0370.3650.0720.0180.0480.0200.1930.0560.0200.0000.0240.0000.0060.0780.0180.0990.0000.1030.0000.0000.0000.0000.0420.0000.0750.0000.0970.0000.0000.0000.0000.0000.0280.0000.1320.0760.0000.0900.0490.0000.0000.2280.1120.0000.0000.0000.0000.0180.0000.1350.0690.0000.0870.0000.0500.0000.0000.2040.1090.0000.0000.2010.3701.0000.2460.0000.2130.2980.0000.0230.0790.0000.0930.0960.1690.2460.0160.0000.0000.3410.0310.0430.2100.0220.0000.0000.0250.1060.1040.0000.1450.0530.0710.1850.0480.0450.0000.0000.0000.0000.0000.0320.0820.0000.0280.0000.0000.0000.0000.2340.0270.1050.0000.1470.0720.0720.0500.0210.0000.0000.0690.2650.0350.1210.2410.0500.0000.0000.0000.0000.0000.0000.0000.1070.0000.0960.0000.1320.1600.0790.1830.0000.0430.0000.0000.0970.0000.0000.0000.0000.0730.0190.0000.0000.0000.0000.0000.3210.0000.2460.0440.0000.0000.0000.1910.3240.0740.074
ExterQual_Ex0.0970.1780.0580.6790.1500.2250.2790.3210.0000.3350.4010.3780.2160.0000.2990.0000.2760.2440.3710.1000.2050.0240.0670.0310.0770.0000.0650.0000.2240.0680.0000.1710.3520.0320.0000.0000.0000.0420.0480.0000.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0530.0000.0000.0000.0000.0000.0000.0500.0000.0000.0000.4090.0230.0000.0250.0150.0000.0700.0000.0000.0000.0000.0060.0000.0210.0540.0000.0220.0000.0000.0000.0000.0190.0000.2320.0000.2530.0000.0000.0000.0000.0000.0000.0000.2260.0610.0000.0000.0390.0000.0000.0300.0560.0000.0000.0000.0000.0000.0000.2290.0560.0000.0000.0000.0500.0000.0000.0240.0540.0000.0000.0470.1960.2461.0000.0000.1300.2410.0190.0000.0000.0000.0000.0370.1320.1650.0000.0000.0000.5250.0000.1280.1360.0000.0510.0000.0000.0270.1610.0240.0930.0220.0520.1670.0250.0480.0000.0060.0000.0000.0000.0100.0300.0000.0000.0000.0000.0000.0000.1610.0000.0430.0000.1170.0340.0340.0340.0000.0000.0000.0460.5550.0000.0900.1730.0000.0000.0000.0000.0000.0000.0000.0000.0500.0000.1270.0000.0990.2230.0260.1330.0000.0000.0000.0000.0380.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0000.2950.0000.2240.0370.0000.0000.0000.1630.2900.0510.051
ExterQual_Fa0.1120.0300.0000.4290.3250.1340.1590.0480.0000.0000.0790.0000.0000.1750.1110.2900.2880.0880.2340.0000.0000.0000.0000.0000.0000.0620.0300.0000.1000.0290.0670.0610.1850.0630.2030.0000.0870.1090.0400.1320.0000.0940.1620.0000.0000.0000.0000.0000.0000.0000.0000.0650.0000.0000.0000.0000.0000.1840.0000.0000.0190.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0320.1540.0000.0000.0000.0000.0000.0000.0310.0190.0330.0000.0000.0000.0320.0000.0410.0000.0000.0000.0000.0000.0000.1290.0000.0180.0000.0000.0000.0000.0000.0000.0440.0000.0750.0000.0000.0000.1290.0000.0140.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0510.0700.0000.0001.0000.0570.1160.0000.2670.0000.0000.0770.0000.0220.0610.0650.0000.0000.0000.0470.0570.0190.1220.0000.0880.1150.0000.0000.0000.0000.0000.0000.0480.0510.0000.0260.0000.0000.0030.0000.0000.0170.0000.0000.0000.0350.0000.0000.0590.0750.0000.0000.0000.2150.2150.0700.0590.0000.0000.1040.0000.3100.0690.0000.0950.0000.0000.0000.0000.0000.0660.0000.1090.0000.0000.1240.0000.0160.0300.0000.0000.0000.0000.0000.1150.0000.0000.0000.0000.1240.0000.0000.0000.1240.0000.0480.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.000
ExterQual_Gd0.3820.1210.0000.6790.4010.6740.5620.1270.0980.2750.3430.2580.2890.0000.3550.0750.2450.6360.3780.2800.2200.1440.0590.0720.0000.0000.0920.0730.4750.1910.1360.2480.4470.0000.0430.2360.0470.0580.1790.1230.0000.0000.0690.0100.0540.0000.0190.0000.1440.0000.0620.1080.0000.2780.0100.1410.0870.0970.0650.0840.2430.0380.0820.2160.1390.1570.0600.1450.0910.2750.1330.0850.0590.0250.0960.1300.0060.1790.1580.0390.0000.0000.0460.2150.0870.0670.0110.0290.0000.0140.0000.0000.0730.0000.0000.0210.0000.0000.1260.0000.1560.1110.0000.0480.4650.1660.0380.0590.0000.0280.0000.0000.0000.1080.0000.1500.0000.1450.0000.0510.4640.1740.0130.0250.1290.2510.2130.1300.0571.0000.9050.0000.0900.0590.0000.1030.1840.4520.5850.0700.0000.0000.1350.0930.4990.5090.0930.0480.0000.0690.1830.0210.0000.1350.1560.1660.3740.0970.1800.0610.0000.0360.0000.0780.0850.1600.0000.0560.0000.0280.0000.0000.4960.1250.1390.0000.3740.1580.1580.1750.0880.0000.0000.2080.0700.1100.6270.6160.0070.0000.0750.0710.0250.0000.1000.0220.2880.0560.1380.0100.2810.2700.2730.4110.0000.0820.0000.0000.1720.0000.0830.0000.0280.1910.0530.0000.0000.0000.0000.0000.2280.0000.1480.0650.0000.0000.0300.0870.2250.1690.169
ExterQual_TA0.3940.1640.0000.7220.4320.7200.6140.2150.1020.3000.4160.3170.3010.0000.4050.1010.2790.6970.4380.2990.2620.1570.0560.0480.0330.0480.1080.0750.4980.2060.1320.2820.5240.0000.0000.2300.0230.0520.1850.0890.0340.0000.0100.0160.0500.0000.0040.0000.1300.0000.0710.1020.0190.2380.0140.1400.0740.0620.0740.0990.2680.0460.0950.2120.2910.1580.0600.1630.0750.2740.1610.0850.0480.0300.0660.1360.0000.1840.1770.0490.0000.0000.0080.2090.0910.0860.0260.0540.0080.0740.0000.0000.0690.0000.0000.0430.0000.0910.1580.0000.1520.1290.0000.0350.4660.1750.0390.0420.0000.0350.0000.0000.0940.1390.0000.1440.0000.1670.0000.0510.4600.1850.0000.0360.1340.3040.2980.2410.1160.9051.0000.0000.0330.0750.0000.0910.1950.4840.6190.0560.0000.0000.3300.0860.4190.5410.0740.0690.0000.0330.1900.0980.0000.1700.1720.1880.4170.0970.2040.0570.0000.0400.0000.0930.0940.1650.0000.0580.0140.0000.0000.0080.5310.1150.1540.0000.4050.1260.1260.1720.0770.0000.0000.2010.2810.0490.5560.6700.0380.0000.0770.0740.0000.0000.0880.0290.2790.0530.1830.0000.3140.3420.2400.4550.0000.0970.0000.0000.1610.0000.0960.0000.0350.1810.0690.0000.0000.0000.0000.0000.3320.0000.2370.0780.0080.0000.0420.1530.3270.1840.184
ExterCond_Ex0.0000.0000.0000.1110.3590.2320.0000.0000.0000.0400.0000.0460.0600.0000.0370.0000.0180.0770.0540.0000.3340.0000.0000.0000.0000.0000.0000.0410.0830.0000.0000.0000.0310.0240.0000.0000.0000.0180.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0790.0000.0000.0000.0000.0160.0790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.0000.0001.0000.0000.0000.0000.0950.0550.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0680.0000.0540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
ExterCond_Fa0.2050.0000.0000.2970.3380.1960.1720.0510.0000.0000.1000.0000.0310.1950.0000.0690.0000.1390.1940.0500.0000.0340.0000.0000.0000.0000.0140.0000.0850.0560.0490.0630.1960.0000.0750.0000.0000.0630.0550.0000.0000.0000.0070.0000.0040.0000.0000.0000.0000.0000.0000.0830.0000.0290.0000.0440.0000.1820.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0000.0000.0290.2030.0000.0000.0170.1200.0000.0000.0000.0000.0200.0000.0000.0000.0000.0700.0000.0000.0000.0400.0000.0570.0000.0000.0000.0000.0000.0000.0000.0000.1130.0500.0260.0000.0870.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0710.0480.0000.0000.0000.0480.0650.0230.0000.2670.0900.0330.0001.0000.0280.0000.3670.1080.0000.0860.1170.0150.0000.0200.0540.0910.0550.1610.0000.1960.1570.0280.0000.0000.0000.0270.0000.0670.0000.0000.0390.0000.0000.0000.0000.0000.0000.0000.0150.0000.0950.0000.0000.0930.0950.0000.0000.0390.1940.1940.0710.1070.0410.0880.1590.0100.2390.1060.0350.1110.0230.0000.0000.1060.0000.1070.0000.0900.0000.0000.0000.0000.0680.0560.0300.0000.0000.0000.1570.1400.0000.0540.0000.0950.2050.0000.0000.0000.1460.0000.0230.0200.0000.0000.0000.0310.0000.0000.0000.0210.0540.054
ExterCond_Gd0.1240.0000.0600.0820.3660.2080.0890.0290.0740.0800.0530.0000.0700.0560.1270.0000.0320.1680.0730.0730.0000.0920.0450.0000.0790.0000.0000.0830.0560.0630.0000.0460.1310.0000.0000.0610.0000.0170.0770.0000.0000.0000.0000.0410.0000.0000.0000.0360.0000.0000.0000.0000.0000.0320.0880.0000.0590.0000.0000.0200.0000.0000.0210.0000.0690.1190.0000.0340.0290.0740.0000.0000.0260.0720.0000.0210.0000.0620.0700.0000.0000.0000.0000.0480.0490.0000.0470.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1090.0000.0000.0240.0770.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1070.0000.0000.0000.0000.0670.0000.0000.0000.0000.0700.0790.0000.0000.0590.0750.0000.0281.0000.0000.8910.0790.0790.1270.0000.0000.0000.0750.0140.0800.1290.0460.1640.0000.1220.0240.0000.0030.0000.0820.0100.0450.0190.0000.0460.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0580.0000.0000.0000.0000.0480.0270.0000.0000.0690.0310.0000.0000.0080.0000.0000.0000.0000.0000.0230.0000.0000.0520.0000.0180.0000.0770.0450.0530.0990.0000.0000.0000.0000.0000.0000.0140.0390.0000.0000.0140.0000.0000.0000.0000.0000.0930.0000.0680.0000.0000.0000.0000.0510.0940.0000.000
ExterCond_Po0.0000.0000.0000.2080.4410.0580.0680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0740.0730.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0950.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0720.0000.0000.0000.0000.0000.0000.1240.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
ExterCond_TA0.1730.0220.0400.1580.3910.2620.0910.0210.0470.0790.0830.0240.0820.0730.1350.0000.0000.2170.1370.0670.0680.1140.0270.0000.0660.0000.0000.0680.0940.0890.0000.0630.1760.0000.0190.0710.0000.0600.1080.0000.0000.0000.0000.0380.0000.0000.0000.0180.0160.0000.0130.0640.0000.0550.0790.0080.0710.0890.0000.0130.0000.0000.0000.0000.0790.1200.0000.0200.0120.0850.0000.0100.0100.0490.0790.0190.0000.0770.1180.0000.0210.0000.0000.0620.0300.0000.0340.0000.0720.0210.0000.0000.0000.0000.0000.0000.0000.0160.0190.0000.1000.0000.0000.0840.0990.0430.0000.0000.0000.0000.0000.0000.0140.0000.0000.1000.0000.0000.0000.0260.0880.0210.0000.0000.0470.1040.0930.0000.0770.1030.0910.0950.3670.8910.0151.0000.1350.0630.1640.0000.0000.0000.0890.0520.1240.1550.1180.1350.0660.1800.0490.0000.0000.0260.0500.0160.0810.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0630.0000.0000.0620.0620.0000.0000.0000.0150.0000.0490.0840.0330.0320.0000.0000.0000.0000.0490.0150.0080.0000.1020.0000.0380.0000.0880.0810.0840.1110.0000.0000.0000.0450.0740.0000.0000.0270.0000.0840.0100.0000.0000.0270.0000.0000.1060.0000.0560.0000.0000.0000.0000.0510.1070.0000.000
Foundation_BrkTil0.3380.0800.0000.1600.2930.6980.3500.1870.0000.1810.1770.1610.2370.1380.0890.1270.0000.5290.2690.1550.1330.3590.0000.0840.0000.0000.1390.0000.1590.1070.0350.0760.2230.0350.0000.0610.0580.2870.3430.1230.0000.0160.0420.0260.0790.0110.0000.0000.0000.0000.0000.2990.0000.1060.1270.0530.0700.0800.0000.0490.1300.0000.0660.0420.0690.3600.1740.0560.0440.0740.0240.0390.0000.0850.0810.0520.0440.0890.2900.1410.1440.0790.1400.0530.0380.0610.0000.0870.0860.1070.0000.0000.0840.0000.0000.0000.0000.0460.1290.0000.0950.0860.0000.1560.1850.2740.0200.0640.0000.0000.0000.0000.0450.1230.0000.0870.0000.0780.0000.1860.1800.2710.0620.0000.2070.2590.0960.0370.0000.1840.1950.0550.1080.0790.0000.1351.0000.2890.2940.0220.0000.0000.0840.1920.1980.1990.1830.0000.0760.0840.1290.0900.0030.1520.0550.0000.1970.0190.0320.2110.0000.0090.0000.0310.0230.0810.0000.2220.1570.1570.0760.0000.1090.1450.0780.0000.0000.2860.2860.1380.0770.0000.0230.1760.0310.1040.1430.1250.0000.0740.0000.0180.0000.0000.0530.0000.3460.0000.0540.0000.3760.1560.1880.2720.0000.3150.0000.0550.2820.0000.1920.0000.0890.1970.0000.0000.0000.0000.0000.0290.0840.0000.0530.0160.0000.0000.0000.0220.0860.1250.125
Foundation_CBlock0.3450.0590.0220.4920.3560.7760.5740.1300.2100.2430.1790.0850.2310.0000.2940.0440.2360.7050.2420.2110.1770.0300.0000.0810.0230.0000.0410.1180.4000.1350.0580.1460.3590.0000.0250.1840.0000.1320.0440.0000.0570.0650.0000.0000.0030.0380.0200.0000.0850.0000.1100.0630.0580.1970.0000.0290.1930.0000.0610.0300.4000.0770.2000.1340.1960.0290.0740.1960.0340.2150.0740.0000.0000.0000.0000.1160.0170.1260.0000.0000.1190.0000.0000.2200.1060.1200.0500.0760.0000.0620.0130.0000.0000.0000.0000.0440.0000.0090.2690.0000.1010.2130.0000.0510.4280.0300.0210.0000.0250.0420.0290.0000.0180.2390.0000.0960.0000.2540.0000.0660.4280.0450.0000.0780.0480.0270.1690.1320.0220.4520.4840.0000.0000.0790.0000.0630.2891.0000.7800.1050.0370.0000.2500.0000.3400.5070.0000.0600.0000.0690.0580.0000.0160.0970.2500.2470.3110.1060.2330.2330.0300.1290.0000.1520.1450.2050.0000.0440.0000.0160.0000.0200.4340.0000.0690.0000.4110.0270.0270.0900.0000.0000.0000.0680.1730.0000.3630.4510.0000.0000.0930.0460.0070.0000.0950.0310.0000.0080.1410.0380.0410.2320.0710.2420.0000.0420.0000.0000.0000.0000.0000.0100.0160.0420.1430.0000.0000.0000.0000.0000.2560.0000.1380.0700.0000.0000.0000.1240.2500.0500.050
Foundation_PConc0.4340.0830.0000.6090.5160.8280.6080.1740.1600.2620.3100.2000.2550.0650.3370.0700.2310.7690.3850.3160.2600.1850.0000.0640.0130.0000.0920.0860.4920.2140.1480.2090.4940.0000.0600.2370.0220.0330.1570.0870.0740.0410.0290.0340.0000.0620.0000.0000.1120.0000.0830.1110.0290.2810.0630.0940.2390.0830.0290.0000.3280.0560.1420.1760.2540.1770.0000.1570.0520.2760.1080.0410.0000.0270.0840.1430.0000.1990.1990.0450.0250.0000.0470.2630.0740.0640.0400.0000.0000.0000.0450.0000.0450.0000.0000.0840.0000.0600.1820.0000.1600.1630.0000.0410.5600.2040.0680.0580.0000.0450.0280.0000.0640.1570.0000.1510.0000.2050.0000.0450.5600.2240.0270.0650.0840.2210.2460.1650.0610.5850.6190.0000.0860.1270.0000.1640.2940.7801.0000.1070.0380.0000.3190.0860.4920.6020.1200.0740.0000.0750.1540.0870.0000.1480.1910.2320.4520.1250.2450.1150.0240.1000.0000.1150.1100.2290.0000.1260.0900.0450.0000.0210.5290.1290.1290.0000.4180.1860.1860.1900.0830.0000.0000.2110.2120.0700.4770.5520.0000.0310.1140.0930.0500.0000.1660.0380.2570.0240.1870.0560.2890.3520.2010.4320.0000.1270.0000.0000.1920.0000.1050.0010.0180.1840.1320.0000.0000.0010.0000.0000.3250.0000.1890.0840.0000.0090.0100.1540.3200.1450.145
Foundation_Slab0.2610.0290.0000.2540.1510.1670.1420.0800.0000.2050.4230.0000.0000.0000.0000.0910.0660.1190.0700.0000.0200.0400.0000.0000.0000.0000.0990.0000.0000.0930.2550.0410.0630.0690.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0780.0000.0000.0000.0000.0330.0000.0000.0000.0000.0080.0000.0000.0640.0000.0000.0000.0000.0920.0000.2500.0000.0090.0000.0000.0420.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0630.0750.0000.0000.0000.0000.0000.0240.0000.0000.0360.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0470.0000.0140.0000.0200.0520.0160.0000.0650.0700.0560.0000.1170.0000.0000.0000.0220.1050.1071.0000.0000.0000.0130.0000.1020.1070.0000.0000.0000.3740.0390.0180.0090.1700.0390.0220.0710.0000.0180.0730.0000.0000.0000.0000.0000.3120.0000.1440.0000.0000.0000.3530.0790.1380.0000.0000.0320.1490.1490.0340.1190.0000.0000.1010.0000.0560.0850.0760.0000.0000.0260.0210.1180.0000.1000.0000.0700.0000.0000.0890.0280.0610.0460.0780.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.0130.0000.0050.0000.0000.1350.0000.0000.0150.0400.040
Foundation_Stone0.0280.0000.0000.0000.1030.2400.0000.0000.0000.0430.0000.0000.0830.0000.0430.0660.0990.1700.0000.0000.0990.4170.0000.0000.0000.0000.0290.0000.0000.0000.0780.0000.1850.0050.0540.0000.0000.0520.0400.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0770.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0180.0000.0000.0290.0000.0000.0000.0000.0000.0000.0680.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0250.0000.0180.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0240.0000.0000.0000.0160.0330.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0370.0380.0001.0000.0000.0000.0000.0360.0300.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0590.0000.0000.0000.0000.0000.0000.0000.0060.0320.0000.0000.0000.0200.0000.0350.0000.0000.0880.0880.0000.0000.1120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1120.0000.0000.0000.0000.0680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Foundation_Wood0.0490.1800.0000.0000.0000.1160.0000.0000.0000.0000.0000.0000.0320.0000.0000.0780.0000.0000.0000.0000.0000.0000.5720.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0520.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.035
BsmtQual_Ex0.1820.2270.0000.6560.2380.3860.3740.3950.0170.2530.4340.4140.2090.0000.3140.0490.2640.3950.4430.1930.2010.0840.0000.0000.0470.0550.1450.0510.2630.0600.0490.2080.4930.0330.0000.0000.0000.0620.0760.0000.0830.0000.0000.0000.0080.0260.0000.0260.0000.0000.0000.0480.0000.0190.0000.0080.0210.0310.0000.0400.1220.0000.0450.0180.4560.0780.0150.0580.0340.0620.1200.1120.0000.0000.0240.0440.0150.0500.0870.0000.0720.0000.0000.0000.0310.0390.0000.2270.0000.2540.0000.0000.0000.0000.0000.0420.0000.1270.0930.0000.0470.0760.0000.0000.2000.0860.0000.0000.0000.0000.0150.0000.1420.0860.0000.0440.0000.0910.0000.0000.2010.0900.0000.0000.0510.2470.3410.5250.0000.1350.3300.0000.0200.0750.0000.0890.0840.2500.3190.0130.0000.0001.0000.0290.2540.2650.0380.0560.0000.0000.1090.1910.0000.1830.0920.0850.2450.0320.0870.0000.0000.0270.0000.0390.0450.0850.0000.0250.0000.0000.0000.0000.2680.0000.1070.0000.1890.0700.0700.0580.0180.0000.0000.0750.5130.0330.0000.2590.0000.0000.0000.0280.0000.0000.0510.0000.1310.0000.1250.0000.1640.3150.0000.2340.0000.0410.0000.0000.0850.0000.0290.0000.0000.0780.0370.0000.0000.0000.0000.0000.3800.0000.2810.0400.0000.0000.0000.2170.3730.0900.090
BsmtQual_Fa0.1020.0000.0000.1600.2360.3110.1800.0610.0000.0900.0930.0700.0590.0570.0000.0860.0540.2670.0610.0540.0000.1590.0290.0000.0000.0000.0630.0000.0710.0550.0280.0830.0640.0000.0000.0000.0000.0960.1260.0600.0000.0000.0110.0260.0180.0000.0230.0000.0000.0000.0000.0000.0000.0370.0170.0480.0090.0000.0000.0000.0250.0000.0000.0000.0060.2630.0000.0000.0000.0140.0000.0000.0000.0000.0820.0000.0000.0270.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0600.0240.0000.0600.0690.0800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0120.0000.0580.0660.0850.0000.0000.0750.0950.0310.0000.0470.0930.0860.0000.0540.0140.0000.0520.1920.0000.0860.0000.0000.0000.0291.0000.1270.1330.1640.0000.1740.0840.0540.0040.0140.0460.0230.0000.0910.0240.0020.1470.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0690.0000.1140.0000.0000.0910.0910.0920.0000.1390.0770.1330.0000.0660.1020.0750.0000.1030.0000.0430.0000.0000.0870.0000.1280.0000.0150.0000.1420.0680.0810.1340.0000.1830.0000.1390.1600.0000.1340.0000.1490.1420.0000.0000.0000.0000.0000.0000.0290.0000.0100.0260.0000.0000.0000.0000.0300.0530.053
BsmtQual_Gd0.3770.0000.0300.5310.3690.6520.4690.1060.0780.1800.2090.1520.2680.0920.2430.1160.1490.5270.3370.2510.1470.1910.0000.0340.0000.0000.0740.0400.4540.2000.1310.1760.4200.0000.0570.1930.0350.0820.1800.1070.0200.0000.0290.0630.0730.0450.0000.0000.0910.0000.0790.1400.0000.3050.0160.1110.2260.1220.0490.0310.2820.0400.0240.1420.0000.2080.0600.1290.1560.2050.0460.0390.0370.1200.0790.0500.0630.2120.2050.0730.0900.0470.0230.2340.1010.0000.0000.1610.0430.1610.0000.0000.0790.0000.0000.0440.0000.0190.0310.0000.1710.0620.0000.0930.3550.2080.0630.0790.0000.0000.0000.0000.0120.0000.0000.1630.0000.0000.0280.0850.3500.2090.0510.0460.1120.1310.0430.1280.0570.4990.4190.0000.0910.0800.0000.1240.1980.3400.4920.1020.0360.0270.2540.1271.0000.7650.1300.0470.0000.1090.1560.0500.0070.1250.0630.1590.3570.0830.1850.0000.0170.0320.0260.0000.0870.1370.0000.0610.0290.0130.0000.0180.2900.1060.0680.0000.2110.1710.1710.1860.0770.0000.0000.2100.0890.1000.4480.3560.0000.0280.0580.0770.0000.0000.0940.0360.2620.0000.1090.0000.2890.1910.2460.3470.0000.0960.0000.0000.1520.0000.0990.0000.0420.1530.0570.0000.0000.0000.0000.0000.0750.0000.0000.0730.0000.0000.0000.0000.0730.1770.177
BsmtQual_TA0.3980.0730.0240.5560.4630.7410.5310.1910.1470.1860.3080.2250.2690.1020.3230.1050.2120.6500.3850.2860.2010.2060.0000.0470.0000.0000.0910.0790.5440.1790.0560.2240.5170.0000.0800.1880.0360.0980.1900.0950.0610.0000.0000.0490.0760.0600.0210.0070.0870.0000.1080.1510.0000.2490.0250.0600.1850.1470.0040.0000.3480.0040.0000.1460.2060.1750.0960.1780.1430.2190.1100.0950.0280.1400.0590.0000.0140.2150.2320.0850.0190.0480.0510.2270.0650.0270.0000.0290.0510.0130.0000.0000.0610.0000.0000.0570.0000.0830.1080.0000.1820.0000.0000.0510.4220.2360.0440.0860.0000.0000.0250.0000.0880.0720.0000.1790.0000.0140.0000.0670.4150.2280.0410.0610.0990.2100.2100.1360.0190.5090.5410.0240.0550.1290.0000.1550.1990.5070.6020.1070.0300.0000.2650.1330.7651.0000.1130.0640.0000.0520.1780.1360.0000.2820.1550.2350.4300.1080.2640.0000.0260.0780.0280.0490.1400.0740.0000.0420.0500.0400.0000.0210.3820.0860.0940.0000.2990.1160.1160.1710.0380.0000.0000.1620.1720.0740.3900.4450.0000.0000.0370.0620.0000.0000.0600.0550.2580.0000.1640.0000.3100.3210.1900.4070.0240.0740.0000.0000.1350.0000.0670.0000.0000.1260.0810.0000.0000.0030.0000.0000.2610.0000.1580.0980.0000.0340.0480.1080.2550.1850.185
BsmtCond_Fa0.1140.0420.0000.2050.2610.2860.1320.0550.0000.0720.0570.0000.0880.1960.0000.1560.1360.2220.1230.0000.0150.2030.0000.0000.0000.0000.0730.0000.0670.0720.0000.0300.1470.0000.0510.0120.0720.1030.0940.0420.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0230.0000.0470.0000.0640.0220.0810.0000.0000.0000.0000.0000.0000.0200.0700.1110.0190.0000.0250.0000.0000.0000.0000.0650.0000.0000.0360.0830.0350.0480.0620.0460.0000.0000.0120.0000.0000.0000.0000.0000.0000.0590.0000.0390.0000.0000.0080.0260.0000.0000.0090.0000.0000.0460.1130.0000.0590.0000.0000.0000.0000.0060.0200.0000.0000.0000.0000.0000.0440.0520.1060.0000.0000.0560.0840.0220.0000.1220.0930.0740.0000.1610.0460.0000.1180.1830.0000.1200.0000.0000.0000.0380.1640.1300.1131.0000.0120.0000.5220.0650.0250.0000.0710.0520.0000.1050.0910.0390.0940.0000.0000.0000.0000.0000.0340.0000.0910.0650.0000.0390.0000.1110.0840.0000.0000.0670.2170.2170.1530.0420.0000.0000.1620.0000.1280.0980.0650.0000.0000.0000.0590.0760.0000.0810.0000.1290.0000.0000.0000.1010.0630.0880.1080.0000.0620.0000.0000.0870.0000.1640.0560.0000.1820.0000.0000.0000.0560.0000.0870.0390.0000.0000.0000.0000.0000.0000.0050.0400.0510.051
BsmtCond_Gd0.0650.0000.0000.0710.0880.0790.1240.0000.0730.0700.0000.0000.0760.0000.0750.0000.0400.0480.0610.0680.0000.0000.0000.0000.0000.0190.1170.0980.0720.0000.1170.0310.0690.0970.0000.0000.0000.0340.0410.0000.0000.0000.0000.0000.0360.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0300.0130.0000.0000.0000.0540.0000.0000.0000.0000.0350.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0320.0000.0000.1000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0000.0000.0540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.0500.0180.0000.0000.0000.0000.0000.0510.0000.0480.0690.0000.0000.1640.0000.1350.0000.0600.0740.0000.0000.0000.0560.0000.0470.0640.0121.0000.0000.6340.0220.0310.0000.0320.0000.0220.0430.0000.0100.0000.1040.0000.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.0500.0000.0120.0000.0200.0430.0430.0250.0000.0000.0000.0410.0300.0000.0260.0480.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.0250.0000.0240.0370.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0810.0000.0400.0000.0000.0240.0000.0630.0780.0540.054
BsmtCond_Po0.0000.0000.0000.4940.7710.0940.0340.0000.0000.0000.0000.0000.0000.0000.0000.0720.0000.1010.0190.0000.0000.0000.0000.0000.0000.0280.0000.0000.2300.0000.0000.0000.0590.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0880.0000.0000.0000.1960.0000.0000.0660.0760.0000.0000.0000.0000.0000.0000.1740.0000.0000.0000.0001.0000.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0000.0090.0090.0100.0000.0000.3520.0840.0000.0440.0000.0000.0880.1540.0000.0000.0000.0000.0960.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2010.0750.0000.0000.0000.1290.0800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.000
BsmtCond_TA0.1400.0620.0000.2040.2110.1740.1140.0990.0000.1570.2840.0000.0970.0800.0170.1380.0700.1490.0850.0000.0000.0580.0000.0000.0000.0000.1160.0430.0240.0920.1710.0360.0980.0750.0000.0000.0310.0000.0000.0000.0190.0050.0000.0080.0000.0000.0000.0000.0000.0000.0000.0140.0000.0430.0000.1530.0000.0290.0000.0000.0100.0000.0000.0000.0000.0000.0440.0170.0000.0000.0000.0000.0000.0340.0260.1340.0000.0230.0100.0000.0000.0000.0000.0000.0000.0000.0720.0000.0000.0000.0000.0000.0400.0220.0750.0140.0000.0470.0000.0000.0000.0000.0000.0220.0000.0330.0000.0110.0000.0000.0000.0000.0460.0000.0000.0090.0000.0000.0280.0000.0200.0410.0000.0000.0680.0950.0250.0000.1150.0690.0330.0000.1570.1220.0000.1800.0840.0690.0750.3740.0170.0000.0000.0840.1090.0520.5220.6340.0751.0000.0440.0000.0000.0950.0500.0510.0770.0440.0000.0000.0440.0000.0000.0000.0000.2120.0220.1090.0220.0000.0000.1310.0780.1040.0000.0000.0410.1980.1980.0970.0930.0000.0220.1480.0000.1170.0800.0400.0000.0280.0000.0550.0850.0000.0980.0000.1140.0000.0000.0370.0720.0360.0780.0720.0000.0000.0000.0000.0640.0000.0990.0370.0000.1450.0000.0000.0000.0370.0000.0280.0000.0000.0000.0000.0390.0780.0150.0000.0000.0120.012
BsmtExposure_Av0.2780.0000.0000.1500.0940.2540.2110.0940.0000.1110.1250.0850.1620.0000.0790.1500.1600.2210.1650.2600.0000.0830.0000.0300.0760.0000.1410.0000.1030.0970.0350.0190.1500.0540.0000.0000.0000.0720.0790.0110.0000.0000.0000.0000.0320.0000.0200.0000.0650.0000.0240.0660.0000.2180.0480.0340.0000.0000.0450.0590.0890.0000.0300.0000.0890.1160.0210.0000.0340.0000.0480.0370.0000.0000.0000.0000.0000.0600.1140.0000.0000.0000.0000.0560.2280.1900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0460.0000.0310.0000.0000.0970.0000.0000.0410.1600.0880.0000.0000.0000.0000.0210.0000.0340.0000.0000.1040.0000.0000.0000.0420.1680.0990.0000.0000.0480.1030.1060.0270.0000.1830.1900.0000.0280.0240.0000.0490.1290.0580.1540.0390.0000.0000.1090.0540.1560.1780.0650.0220.0000.0441.0000.1280.1160.5770.0000.0270.1940.0320.0940.0730.0080.0000.0070.0000.0260.0140.0000.0350.0000.0000.0000.0000.1210.0450.0500.0000.0630.0550.0550.0630.0000.0000.0000.0740.1060.0310.0930.1340.0180.0000.0330.0000.0210.0000.0520.0000.1090.0080.0320.0000.1360.0910.0830.1450.0000.0560.0000.0000.0650.0000.0540.0000.0000.0590.0370.0000.0000.0000.0000.0000.1430.0000.0760.0000.0000.0240.0000.0900.1380.0560.056
BsmtExposure_Gd0.1410.1560.2620.2540.1150.1460.1330.3860.1140.1540.2950.2760.1550.0000.1640.0780.0430.1020.1890.1850.0490.0430.0530.0460.0190.0220.2990.0700.1190.0000.0770.2180.1940.0300.0000.0570.0000.0880.0430.0050.1660.2620.2170.0320.0710.0000.0000.0000.0000.0000.0000.0220.1870.0000.0000.0000.0000.0120.0000.0000.0000.0000.0500.0290.0630.0740.0200.0380.0000.0590.0310.1470.1200.0000.0000.0400.0220.0000.0780.0000.0270.0000.0000.0520.0880.1180.2080.1130.0000.0680.0000.0000.0080.0000.0000.0000.0000.0000.0190.0000.0440.1200.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0700.0480.0260.0880.0000.0000.0000.0000.0000.0000.0650.1270.1040.1610.0000.0210.0980.0000.0000.0000.0000.0000.0900.0000.0870.0180.0000.0000.1910.0040.0500.1360.0250.0310.0000.0000.1281.0000.0840.4330.0000.0300.2300.0000.0390.1430.1180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0300.0000.0000.0000.0540.0540.0640.0230.0000.0000.0810.1130.0160.0500.1010.0000.0000.0000.0000.0000.0000.0000.0000.1260.0510.0000.0000.1650.1370.0310.1520.0000.0450.0000.0000.0310.0000.0040.0000.0000.0170.0410.0000.0000.0000.0000.0000.0370.0000.0210.0360.0000.0000.0000.0000.0440.1420.142
BsmtExposure_Mn0.0850.0090.0000.0000.0450.0450.0280.0000.0140.0000.0000.0000.0930.0000.0980.0000.0000.0640.1150.0000.0640.0000.0000.0400.0000.0490.0300.0000.0000.0000.0000.0470.0270.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0380.0200.0000.0000.0480.0000.0000.0000.0000.0000.0000.0230.0070.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0160.0000.0000.0000.0670.0410.0100.0000.0000.0000.0000.0000.0000.0000.0290.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0160.0000.0000.0310.0000.0000.0240.0000.0000.0000.0000.0000.0030.0000.0000.0030.0160.0000.0090.0000.0000.0000.0140.0070.0000.0000.0000.0000.0000.1160.0841.0000.3960.0000.0000.0000.0000.0250.0220.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0140.0000.0000.0000.0300.0000.0000.0370.0240.0000.0000.0000.0250.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0930.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.031
BsmtExposure_No0.2940.1000.1270.2330.1310.2280.1770.2470.0720.2380.2550.2240.2210.0450.1060.1460.1380.1970.2410.2550.0490.1010.0000.0000.0000.0000.2370.0480.1480.1060.0230.1180.2380.0410.0000.0420.0000.1310.1150.0280.1090.1650.1240.0000.0950.0000.0160.0360.0410.0000.0650.0580.1210.1510.0310.0200.0000.0160.0000.0540.0610.0410.0810.0000.1050.1420.0510.0310.0000.0000.0830.1080.0360.0530.0270.0770.0580.0330.1190.0420.0390.0000.0000.0930.2060.1720.1040.0520.0000.0360.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0920.0480.0000.0000.0950.0560.0000.0150.0000.0000.0000.0000.0320.0000.0240.0980.0000.0300.0000.0540.0980.0620.0000.0000.0350.1160.1450.0930.0000.1350.1700.0000.0000.0000.0000.0260.1520.0970.1480.1700.0000.0000.1830.0460.1250.2820.0710.0320.0000.0950.5770.4330.3961.0000.0000.0500.2670.0220.0950.2000.1100.0120.0000.0000.0000.1200.0000.0000.0000.0310.0000.0520.1010.0250.0350.0000.0580.0010.0010.0910.0000.0000.0000.0840.1270.0070.1020.1580.0420.0160.0000.0000.0000.0000.0240.0000.1300.0560.0000.0000.1700.1290.0650.1660.0000.0520.0000.0000.0570.0000.0000.0000.0000.0150.0670.0000.0170.0000.0000.0000.1330.0000.0830.0340.0000.0810.0000.0800.1320.1340.134
BsmtFinType1_ALQ0.1610.0000.0810.1940.1730.3540.2600.1530.1570.2270.0770.0760.1170.0000.1210.0390.1260.3060.1050.0550.0900.0000.0000.0810.0450.0400.1380.0770.1230.0000.0350.0000.1240.0000.0000.0620.0230.0900.0360.0100.0180.0000.0350.0220.0000.0530.0000.0490.0000.0000.0280.0180.0000.0580.0000.0000.0570.0000.0000.1150.0840.1480.1520.0600.0730.0390.0000.0490.0000.0810.0000.0000.0310.0000.0000.0220.0220.0000.0550.0000.0640.0000.0000.0730.0000.0960.0000.0000.0000.0000.0000.0000.0320.0000.0000.0170.0000.0000.1580.0000.0150.1380.0560.0210.1340.0250.0000.0320.0360.0920.0310.0000.0000.1080.0000.0250.0000.1470.0000.0000.1320.0130.0000.0000.0000.0400.0530.0220.0000.1560.1720.0000.0270.0820.0000.0500.0550.2500.1910.0390.0000.0000.0920.0230.0630.1550.0520.0000.0000.0500.0000.0000.0000.0001.0000.1360.2630.0890.1270.2690.0300.1200.0000.0790.1330.1230.0000.0000.0000.0000.0000.0000.1200.0000.0000.0000.1060.0460.0460.0710.0000.0000.0000.0810.0510.0110.0970.1380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0480.0000.0000.0450.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.1070.0000.0720.0000.0000.0090.0000.0650.1020.0000.000
BsmtFinType1_BLQ0.1470.0000.0000.1900.1620.2680.2200.0720.1430.1580.0830.0000.0680.0000.1100.0270.0780.2580.1300.1000.0000.0000.0910.0000.0000.0630.0470.0340.2020.0300.0000.0920.2040.0000.0000.0370.0310.0270.0130.0000.0180.0000.0000.0000.0080.0000.0000.0000.0000.0000.0310.0000.0080.0840.0120.0290.0710.0000.0000.0000.1910.0000.0000.0430.0580.0430.0000.0560.0000.0540.0240.0000.0000.0400.0260.0380.0000.0630.0000.0000.0820.0000.0000.0670.0000.0220.0000.0000.0000.0000.0000.0000.0120.0000.0000.0340.0000.0160.0710.0000.0520.0000.0000.0000.1480.0000.0000.0410.0000.0000.0000.0000.0140.0490.0000.0640.0000.0970.0000.0000.1530.0000.0000.0000.0000.0180.0710.0520.0000.1660.1880.0000.0000.0100.0000.0160.0000.2470.2320.0220.0000.0000.0850.0000.1590.2350.0000.0220.0000.0510.0270.0300.0000.0500.1361.0000.2090.0680.0990.2130.0000.0100.0670.0980.1050.0930.0000.0000.0000.0000.0000.0000.1550.0000.0000.0230.1730.0000.0000.0380.0000.0000.0000.0280.0830.0000.1270.1660.0000.0000.0260.0000.0000.0000.0150.0180.0000.0000.0550.0000.0000.0980.0250.1270.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0850.0000.0770.0000.0000.0000.0140.0790.0870.0000.000
BsmtFinType1_GLQ0.3020.0970.0110.4390.3160.5370.4000.4750.0850.2140.3040.2390.1930.0000.1850.1830.0970.4570.3220.2520.1790.1540.0000.0000.0350.0000.4510.0000.2960.0540.0790.1190.3490.0480.0350.0990.0150.0770.1290.0900.0340.0170.0000.0820.0310.0230.0290.0100.0440.0000.0530.1060.0000.2010.0520.0810.0000.0840.0000.0000.1740.0310.0440.1980.1840.1560.0610.0620.0930.1050.1180.0570.0000.1060.0500.0000.0000.1890.1690.0290.0000.0000.0380.0610.1120.0250.0000.0020.0380.0310.0200.0280.0630.0000.0000.0770.0000.0950.0140.0000.0870.0260.0000.0480.2690.1650.0000.0630.0000.0200.0340.0000.0980.0000.0150.0810.0000.0730.0000.0500.2700.1750.0000.0510.1660.2600.1850.1670.0480.3740.4170.0000.0670.0450.0000.0810.1970.3110.4520.0710.0000.0000.2450.0910.3570.4300.1050.0430.0000.0770.1940.2300.0000.2670.2630.2091.0000.1410.1960.4070.0000.0660.0480.0000.0400.1370.0000.0860.0580.0200.0000.0000.3270.1110.0880.0000.2350.1560.1560.1360.0770.0000.0000.1680.2080.0970.3130.3810.0480.0000.0620.0790.0510.0000.1490.0120.2660.0000.0080.0000.2610.2530.1370.3090.0000.1010.0000.0000.1330.0000.0800.0000.0200.1330.0650.0000.0280.0000.0000.0000.1260.0000.0630.0430.0000.0000.0130.0420.1260.1110.111
BsmtFinType1_LwQ0.0910.0640.0000.1100.0780.1620.1290.0660.3220.0440.0230.0000.0160.1680.0000.1300.0960.1450.0550.0000.0600.1020.0000.0430.0000.0000.0250.0400.0600.0000.0000.0430.1090.0000.0000.0330.0000.0260.0690.0440.0000.0000.0440.0000.0000.0000.0000.0000.0000.0210.0000.0000.0390.0180.0000.0000.0230.0000.0000.0220.0000.0000.0000.0000.0400.0620.0470.0000.0000.0440.0000.0000.0220.0000.0000.0000.0000.0000.0840.0000.0000.0380.0000.0220.0000.0000.0550.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.0140.0850.0670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.0820.0920.0000.0000.0320.0630.0480.0250.0510.0970.0970.0000.0000.0190.0000.0000.0190.1060.1250.0000.0000.0000.0320.0240.0830.1080.0910.0000.0000.0440.0320.0000.0000.0220.0890.0680.1411.0000.0620.1440.2060.0280.1840.0200.0740.1520.0000.0000.0000.0000.0210.0000.0640.0000.0000.0000.0620.0000.0000.0820.0000.0000.0000.0750.0180.0000.0730.0850.0000.0000.0810.0000.0470.0470.1010.0000.0620.0000.0450.0320.0950.0380.0550.0900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0590.0000.0000.0000.0000.0000.0000.0290.0600.0000.000
BsmtFinType1_Rec0.1410.0420.0470.2020.1240.3260.2680.0310.1170.1490.0920.0690.0600.0160.0660.0560.0000.2950.1070.1350.0580.0000.0000.1000.0000.0230.0160.0000.1770.0430.0000.0820.1550.0000.0380.0440.0390.0070.0130.0990.0000.0000.0820.0000.0140.0440.0000.0560.0000.0000.0000.0000.0000.0750.0710.0180.0540.0000.0000.0000.1830.0000.0000.0390.0640.0000.0310.0690.0390.0580.0190.0130.0000.0610.0000.0000.0220.0740.0770.0000.0470.0000.0640.0840.0350.0100.0000.0340.0000.0430.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.1000.0000.0000.0920.1590.0760.0280.0000.0000.0000.0000.0000.0400.0180.0000.0910.0000.0090.0000.0890.1650.1050.0000.1660.0000.0000.0450.0480.0000.1800.2040.0000.0000.0000.0000.0170.0320.2330.2450.0180.0000.0000.0870.0020.1850.2640.0390.0100.0000.0000.0940.0390.0250.0950.1270.0990.1960.0621.0000.2000.0260.1170.0000.0370.0490.0060.0000.0000.0000.0000.0000.0000.1500.0060.0240.0000.1200.0260.0260.0620.0000.0000.0000.0630.0570.0000.1720.1960.0480.0000.0090.0000.0000.0000.0320.0240.0390.0000.0600.0000.0780.0940.0000.1100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0870.0000.0500.0000.0000.0000.0000.0300.0890.0370.037
BsmtFinType1_Unf0.2140.0000.0000.1160.0670.3770.2250.4920.1930.6560.0730.1380.2380.0820.1050.1090.1610.2790.0830.0560.0000.1600.0370.0250.0250.0000.4950.1040.0870.0560.0480.1320.0870.0260.0380.0470.0000.1910.1760.0130.0000.0640.0000.0570.0350.0000.0000.0000.0000.0000.0000.1080.0450.0000.0000.0140.1520.0880.0000.0520.1680.0320.0320.0430.0000.1850.0000.0660.0450.0800.0000.0000.0150.0470.0000.0160.0000.0440.1000.0620.1730.0350.0500.1390.0960.0410.0270.0700.0500.0860.0460.0000.0540.0000.0000.0440.0000.0000.1310.0000.0000.0960.0000.0000.1060.0910.0000.0540.0000.0000.0000.0000.0000.1200.0000.0000.0000.1100.0000.0000.1110.0580.0310.0110.1160.1200.0000.0000.0260.0610.0570.0000.0390.0460.0000.0000.2110.2330.1150.0730.0590.0000.0000.1470.0000.0000.0940.0000.0000.0000.0730.1430.0220.2000.2690.2130.4070.1440.2001.0000.0620.0890.0490.1090.1200.2570.0000.0000.0000.0700.0000.0000.0340.0220.0430.0000.1010.0970.0970.0470.0000.0000.0000.0670.0000.0600.0250.0370.0000.0450.0530.0000.0000.0000.0000.0000.1810.0090.1340.0000.1040.0120.0000.0000.0000.1440.0000.0470.1500.0000.0860.0000.0930.1220.0000.0000.0000.0000.0000.0000.1250.0000.1060.0000.0000.0000.0390.1120.1250.0530.053
BsmtFinType2_ALQ0.0490.0000.0000.0000.0730.0440.0000.0740.4690.0690.0980.0110.0540.0310.0620.0000.0000.0310.0000.0730.0000.0250.0000.0000.0000.0000.0640.0690.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0730.0820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0240.0000.0000.0000.0000.0000.0170.0260.0000.1040.0000.0440.0080.1180.0000.1100.0300.0000.0000.2060.0260.0621.0000.0000.0000.0000.0000.2750.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0450.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.018
BsmtFinType2_BLQ0.0170.0000.1030.1240.0000.1160.0830.0530.3630.0500.0000.0000.0000.0000.0000.0290.0180.0830.0000.0730.0000.0000.0000.0000.0000.0000.0570.0000.0650.0360.0000.0560.0330.0000.0000.0000.0000.0430.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0010.0000.0000.0000.0780.0000.0000.0000.0000.0000.0000.0530.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0000.0000.0000.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0510.0320.0000.0000.0000.0100.0000.0000.0000.0360.0400.0000.0000.0210.0000.0000.0090.1290.1000.0000.0000.0000.0270.0000.0320.0780.0000.0000.0000.0000.0000.0000.0000.0120.1200.0100.0660.0280.1170.0890.0001.0000.0000.0000.0000.3700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0160.0160.0000.0000.0000.0000.0000.0000.0000.0470.0580.0490.0000.0510.0000.0000.0000.0530.0020.0000.0000.0120.0000.0000.0400.0150.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0290.0000.0000.0000.0000.0130.0000.0000.000
BsmtFinType2_GLQ0.0530.0870.0000.0000.0000.0320.0230.0180.4250.0000.0000.0000.0000.0000.0000.0540.0960.0470.0000.0000.0340.0730.0000.0000.0000.0190.0790.0000.0000.0000.0000.0000.0040.0180.0000.0000.0000.0040.0400.0000.0000.0000.0000.0000.0380.0000.0000.0310.0000.0880.0000.0000.0000.0000.0000.0000.0000.0000.0840.0000.0190.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0190.0550.0000.0000.0370.0260.0000.0000.0000.0000.0000.0000.0440.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.0000.0000.0000.0880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0330.0540.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0280.0000.0000.0000.0000.0070.0000.0000.0000.0000.0670.0480.1840.0000.0490.0000.0001.0000.0000.0000.2330.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
BsmtFinType2_LwQ0.0590.0470.0000.0690.0890.1550.1960.0510.4800.1900.0000.0350.0430.0000.0190.0000.0000.1480.0330.1210.0490.1360.0000.1160.1860.0440.0520.0480.0000.0000.0000.0000.0660.0140.0000.0130.0000.0540.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0320.0000.0000.0220.0000.0000.0340.1100.0000.0900.0000.0210.0150.0000.0000.0000.0260.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.0390.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0890.0000.0000.0880.0380.0000.0930.0000.0000.0000.0000.0000.0000.0000.0000.0970.0000.0000.0000.0750.0000.0000.0990.0000.0000.0000.0000.0000.0000.0000.0000.0780.0930.0000.0000.0000.0000.0000.0310.1520.1150.0000.0000.0000.0390.0000.0000.0490.0000.0000.0000.0000.0000.0000.0000.0000.0790.0980.0000.0200.0370.1090.0000.0000.0001.0000.0000.4410.0000.0000.0000.0000.0000.0000.1300.0000.0160.0000.1090.0000.0000.0000.0000.0000.0000.0220.0000.0000.0180.0510.0000.0000.0000.0260.0000.0000.0000.0000.0200.0000.0270.0000.0000.0000.0000.0240.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0400.0000.0320.0000.0000.0000.0130.0000.0400.0000.000
BsmtFinType2_Rec0.0630.0000.1470.1020.0840.1850.1090.0360.5260.0920.0000.0580.0000.0000.0000.0000.0070.1690.0000.0000.0730.0000.0580.0820.0000.0430.0220.1060.0530.0510.0000.0470.0630.0000.0000.0210.0000.0560.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0250.0000.0000.0290.0000.0000.0040.1500.0000.0580.0000.0280.0000.0000.0000.0170.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0390.0000.0480.0290.0720.0000.0470.0000.0330.0000.0000.0000.0460.0590.0000.0200.0000.0060.0410.0000.0000.0760.0440.0000.0000.0170.0000.0350.0590.0000.0300.0000.0230.0000.0300.0000.0000.0810.0360.0000.0650.0000.0000.0320.0100.0000.0850.0940.0000.0000.0000.0000.0000.0230.1450.1100.0000.0000.0000.0450.0000.0870.1400.0000.0000.0000.0000.0260.0000.0360.0000.1330.1050.0400.0740.0490.1200.0000.0000.0000.0001.0000.4800.0000.0000.0080.0000.0000.0000.0670.0040.0000.0000.0730.0000.0000.0000.0000.0000.0000.0000.0380.0000.0280.0620.0000.0000.0530.0000.0000.0590.0480.0000.0000.0000.0000.0470.0000.0390.0000.0270.0170.0000.0260.0000.0000.0330.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0460.0000.0000.0000.0000.0000.0000.0150.0470.0000.000
BsmtFinType2_Unf0.1680.0000.0490.2060.1240.3290.2270.1150.7890.3510.1800.0000.1020.0000.0870.0000.0740.2850.0280.0620.0460.0950.0000.0340.0610.0000.0670.0970.0930.0990.0960.0440.1010.0000.0000.0780.0000.1020.0550.0000.0270.0000.0000.0000.0070.0000.0000.0000.0230.0000.0200.0030.0270.0770.0000.0340.0790.0000.0290.0000.1900.0000.0710.0280.0680.0380.0150.0380.0200.0930.0000.0000.0000.0000.0000.1060.0320.0000.0000.0140.0830.0000.0000.1110.0130.0450.0730.0200.0000.0000.0000.0000.0000.0070.0000.0770.0070.0140.0580.0000.0120.1210.0600.0000.1570.0000.0000.0000.0390.0000.0160.0070.0120.0660.0000.0000.0000.1220.0550.0000.1680.0510.0000.0390.0430.0880.0820.0300.0170.1600.1650.0000.0000.0000.0000.0000.0810.2050.2290.3120.0000.0000.0850.0350.1370.0740.0340.0450.0000.2120.0140.0000.0000.1200.1230.0930.1370.1520.0060.2570.2750.3700.2330.4410.4801.0000.0070.0480.0000.0000.0000.1080.1480.0000.0000.0000.1480.0210.0210.0000.0300.0000.0000.0220.0520.0000.1030.1310.0000.0000.1230.0420.0390.0070.1430.0000.0000.0000.0590.0780.0270.0820.0000.0760.0000.0510.0000.0000.0100.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0070.1150.0000.0740.0000.0000.0000.0000.0570.1100.0000.000
Heating_Floor0.0000.0000.0000.2080.0000.0450.0000.0000.0000.0000.0580.0240.0000.0000.0500.0000.0590.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0071.0000.0810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0380.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0770.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Heating_GasA0.1460.0950.1640.2070.1770.2660.2060.0000.0310.0000.0900.0000.0490.1600.0410.1040.0310.2210.1060.0530.1630.1820.0000.1140.0000.0000.0540.0000.0000.0320.0680.0400.1110.0000.0000.0000.0000.0720.0980.0000.0000.0000.0000.0370.0140.0000.0000.0000.0000.0000.0000.0730.0000.0340.0000.0550.0000.0000.0000.0000.0000.0000.0000.0000.0000.1560.0000.0000.0000.0080.0000.0000.0000.0000.0880.0220.0000.0230.1050.0510.0320.0000.1190.0340.0000.0000.0000.0000.0000.0000.0000.0000.0790.0000.0510.0840.0000.0000.0350.0000.0000.0210.0000.1030.0820.0300.0000.0790.0000.0000.0220.0000.0000.0310.0000.0000.0000.0320.0170.2080.0800.0350.0000.0000.0580.0870.0280.0000.0000.0560.0580.0000.0150.0000.0000.0000.2220.0440.1260.1440.0060.0000.0250.0000.0610.0420.0910.0000.0000.1090.0350.0000.0000.0000.0000.0000.0860.0000.0000.0000.0000.0000.0000.0000.0000.0480.0811.0000.7250.4290.1820.3040.1260.3480.0230.0000.0190.4440.4440.0800.1680.0000.0000.1600.0000.1910.0950.0320.0000.0170.0000.0000.0480.0000.0550.0060.1460.0000.0100.0000.1360.0630.0760.0940.0000.0870.0000.0360.1250.0000.0790.0000.0870.1360.0000.0260.0000.0000.0000.0170.0260.0000.0000.0000.0000.0000.0000.0300.0270.0550.055
Heating_GasW0.1290.0440.2260.0000.1410.2200.0840.0000.0820.0000.0000.0000.0710.0000.0840.1300.0000.2210.0000.0000.2160.2400.0000.1690.0000.0000.0330.0000.0000.0000.0000.0130.0880.0180.0110.0000.0000.0660.0800.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.1390.0000.0000.0000.0000.0000.0000.0000.0000.0870.0000.0000.0000.0890.0000.0340.0000.0940.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0750.0590.0000.0000.0000.0000.0000.0000.0000.1020.0710.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0010.0000.2420.0700.0000.0000.0000.0310.0550.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.1570.0000.0900.0000.0320.0000.0000.0000.0290.0500.0650.0000.0000.0220.0000.0000.0000.0000.0000.0000.0580.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.7251.0000.0000.0000.0000.0780.0600.0000.0000.0660.2590.2590.0000.0470.0000.0000.0340.0000.1130.0540.0000.0000.0390.0000.0000.0000.0000.0490.0320.0880.0000.0000.0000.1060.0320.0430.0850.0000.0610.0000.0000.0220.0000.0350.0000.0000.0000.0000.0470.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0300.030
Heating_Grav0.0650.1080.0000.2260.0920.1290.1400.0000.0000.0000.0650.0780.1050.0000.0720.0880.0860.1040.1580.0000.0000.0410.0000.0000.0000.0000.0000.0000.0630.0390.0000.0000.1560.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0500.1430.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0670.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0690.0000.0000.0240.0400.0000.0000.0350.0280.0000.0000.0950.0000.0000.0000.1570.0160.0450.0000.0000.0000.0000.0000.0130.0400.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0200.0000.0000.0700.0000.0000.0000.0000.0000.0000.0000.4290.0001.0000.0000.0000.0550.3440.0000.0000.0210.2420.2420.0790.0970.0000.0000.1350.0000.2020.0390.0000.0350.0000.0000.0000.0000.0000.0000.0000.0690.0000.0000.0000.0000.0090.0210.0000.0000.0000.0000.1030.1550.0000.0000.0000.2090.1650.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Heating_OthW0.0230.0000.0000.0000.0000.2290.0350.0000.0000.0000.0000.0000.0550.7030.0550.0000.0000.0000.0190.0000.0000.1050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0500.0000.0000.0000.0410.0570.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.1820.0000.0001.0000.0000.0000.1450.0000.0000.0000.0990.0990.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0000.0750.0000.0480.0000.0000.0800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Heating_Wall0.1930.0000.0000.2230.1400.1630.0920.0000.0000.0420.1610.0000.0000.0000.0000.0000.0060.0550.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0170.1740.0320.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0000.1650.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0950.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0330.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.1060.0330.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0200.0210.3530.0000.0000.0000.0000.0180.0210.0000.0000.0000.1310.0000.0000.0000.0520.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1080.0000.3040.0000.0000.0001.0000.0300.1700.0000.0000.0000.1700.1700.0610.1360.0000.0000.1450.0000.0000.0140.0290.0000.0000.0000.0000.0540.0000.0000.0000.0430.0000.0000.0000.0680.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.002
HeatingQC_Ex0.3160.0690.0000.4870.3040.6080.5760.1440.0890.2080.2780.2070.2180.0290.2760.0740.2030.6110.2860.2390.1930.1530.0570.0000.0000.0000.1100.0520.3430.1350.1670.1720.3510.0080.0530.1670.0220.0590.1440.0730.0000.0330.0580.0380.0480.0250.0280.0000.0970.0000.0830.0800.0000.2390.0000.0360.0700.0860.0600.0000.2170.0660.1460.1360.2160.1140.0000.0970.0000.2070.0440.0360.0000.0160.0640.1600.0000.1130.1210.0000.0000.0050.0200.1330.0580.0720.0000.0180.0000.0380.0000.0000.0740.0000.0000.0700.0000.0520.2200.0000.0730.1670.0000.0360.4560.1070.0410.0740.0160.0550.0000.0000.0570.2010.0000.0610.0000.1860.0000.0530.4540.1240.0000.0640.0650.1930.2340.1610.0590.4960.5310.0000.0930.0000.0000.0520.1090.4340.5290.0790.0200.0000.2680.0690.2900.3820.1110.0500.0000.0780.1210.0420.0000.1010.1200.1550.3270.0640.1500.0340.0000.0000.0210.1300.0670.1480.0000.1260.0780.0550.0000.0301.0000.1840.4490.0000.6520.2190.2190.1330.0790.0160.0000.1710.2030.1010.4130.4750.0000.0000.0740.0220.0000.0000.0790.0200.1880.0000.1640.0470.2120.2710.1710.3400.0140.0540.0000.0160.1340.0000.0600.0000.0550.1470.1050.0000.0000.0000.0000.0000.2540.0160.1410.0580.0300.0650.0170.1060.2590.1080.108
HeatingQC_Fa0.0140.0900.0000.2040.1560.1860.2210.0430.0000.0000.1320.0440.0510.1280.0000.0000.0000.1950.1220.0820.1000.1070.0000.0000.1310.0520.0350.0000.0920.0350.0540.0140.1090.0000.0000.0170.0000.0280.0720.0000.0000.0180.0350.0040.0000.0000.0000.0000.0000.0000.0000.0150.0000.0500.0510.0570.0250.0160.0000.0000.0000.0000.0240.0000.0240.0620.0000.0000.0110.0280.0000.0000.0000.0000.0000.0250.0000.0390.0000.0310.0000.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.1230.0000.0360.0760.0000.0000.0170.0000.0000.0000.0000.0410.0980.0430.0000.0890.0000.0650.0000.0000.0000.0050.0000.0000.0000.0000.0000.0710.1040.0600.0000.0000.0460.0690.0270.0000.0750.1250.1150.0000.0950.0000.0000.0000.1450.0000.1290.1380.0000.0000.0000.0000.1060.0860.0840.0000.0000.1040.0450.0300.0000.0250.0000.0000.1110.0000.0060.0220.0000.0000.0000.0000.0040.0000.0000.3480.0600.3440.1450.1700.1841.0000.0730.0000.1130.3120.3120.0620.1270.0000.0000.1230.0340.1440.1070.0780.0000.0000.0000.0000.0000.0000.0000.0000.1030.0000.0290.0000.0780.0500.0680.0660.0000.0790.0000.0210.1290.0000.0780.0000.0650.1430.0380.0000.0000.0000.0000.0000.0420.0000.0000.0000.0050.0000.0000.0000.0430.0000.000
HeatingQC_Gd0.1380.0000.0000.1320.1700.2170.1910.0490.0000.0980.0970.0900.1060.0600.0000.0000.0000.2040.0930.1110.0350.1170.0570.0000.0000.0540.0710.0630.0870.0000.0000.0470.0980.0320.0000.0270.0000.0340.0650.0000.0000.0000.0000.0000.0150.0000.0000.0000.0300.0000.0000.0380.0000.1140.0000.0000.0900.0000.0000.0390.0520.0000.0130.0250.0800.1200.0000.0000.0000.0540.0000.0000.0000.0430.0000.0000.0000.0430.0800.0000.0620.0000.0000.0000.0000.0450.0000.0480.0000.0530.0000.0000.0440.0000.0000.0200.0000.0200.0490.0000.0180.0000.0000.0000.0990.0220.0000.0610.0000.0000.0400.0000.0310.0600.0000.0070.0000.0000.0000.0000.1000.0190.0000.0000.0180.0900.1050.0430.0000.1390.1540.0000.0000.0580.0000.0630.0780.0690.1290.0000.0350.0000.1070.1140.0680.0940.0000.0120.0520.0000.0500.0000.0000.0350.0000.0000.0880.0000.0240.0430.0050.0000.0000.0160.0000.0000.0000.0230.0000.0000.0000.0000.4490.0731.0000.0000.2830.0000.0000.0000.0000.0310.0000.0160.0760.0000.0420.0890.0000.0460.0160.0000.0250.0000.0130.0000.0550.0480.0390.0000.0410.0260.0470.0570.0000.0050.0000.0310.0540.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0730.0000.0620.0000.0000.0150.0110.0910.0760.0270.027
HeatingQC_Po0.0280.0090.0000.0000.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0500.0000.0000.1130.0000.0000.0000.1670.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0380.0380.0000.0900.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
HeatingQC_TA0.2560.0000.0400.3780.2230.5230.5190.0630.0960.1430.1650.1160.1680.0000.2430.0770.1920.4950.1710.1630.1650.0600.0000.0330.0340.0000.0380.0000.2590.1200.1380.1350.2690.0000.0600.1330.0310.0000.0600.0850.0340.0000.0290.0000.0000.0000.0000.0000.0570.0260.1100.0230.0000.1390.0000.0000.1420.0580.0580.0440.1840.0900.1150.1020.1460.0000.0000.1190.0000.1560.0500.0220.0000.0730.0380.1790.0000.0550.0490.0000.0360.0000.0000.1210.0580.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2100.0000.0380.1870.0000.0000.3720.0650.0620.0000.0000.0460.0000.0000.0000.1790.0000.0270.0000.2160.0000.0200.3670.0780.0000.0380.0000.1010.1470.1170.0000.3740.4050.0000.0390.0000.0000.0000.0000.4110.4180.0320.0000.0000.1890.0000.2110.2990.0670.0200.0000.0410.0630.0000.0090.0580.1060.1730.2350.0620.1200.1010.0000.0290.0000.1090.0730.1480.0000.0190.0660.0210.0000.0000.6520.1130.2830.0001.0000.1230.1230.0940.0300.0000.0000.1020.1330.0500.3650.4090.0000.0000.0380.0000.0000.0000.0480.0000.1090.0000.1190.0480.1550.2400.1110.2900.0000.0000.0000.0000.0350.0000.0000.0240.0000.0640.0840.0000.0000.0240.0000.0000.1900.0000.0900.0780.0280.1130.0540.0120.1930.0750.075
CentralAir_N0.2540.0000.0000.3740.3150.4380.3780.1520.0000.0600.2230.1440.0360.1260.1580.1600.1120.3420.2760.1520.1010.2290.0000.0000.0000.0000.1060.0160.1030.1300.2450.1960.2830.0000.1610.0430.1160.1980.1800.1260.0000.0000.0670.0180.0450.0310.0000.0000.0000.0000.0000.1070.0060.0810.0000.1180.0360.2120.0000.0320.0390.0000.0470.0250.0500.1870.1010.0130.0200.0540.0000.0220.0000.0770.2390.1190.0270.0670.0620.0690.0180.0000.1840.0060.0210.0430.0000.0000.0510.0000.0000.0000.2180.0380.0090.0000.0000.0220.0810.0000.0190.0520.0000.1010.1310.1660.0000.1700.0000.0000.0000.0000.0210.0660.0000.0000.0000.0580.0490.1620.1340.1500.0000.0000.1330.1810.0720.0340.2150.1580.1260.0000.1940.0000.0000.0620.2860.0270.1860.1490.0880.0000.0700.0910.1710.1160.2170.0430.0090.1980.0550.0540.0000.0010.0460.0000.1560.0000.0260.0970.0000.0160.0000.0000.0000.0210.0380.4440.2590.2420.0990.1700.2190.3120.0000.0380.1231.0000.9940.1720.3240.1390.0000.3380.0480.2910.1830.1120.0000.0000.0000.0330.0640.0000.0720.0400.2820.0000.0550.0000.2020.1370.1570.1510.0000.1440.0000.0760.3000.0000.1840.0000.1200.3340.0000.0000.0000.0630.0000.0490.0700.0000.0210.0180.0600.0000.0000.0000.0710.1080.108
CentralAir_Y0.2540.0000.0000.3740.3150.4380.3780.1520.0000.0600.2230.1440.0360.1260.1580.1600.1120.3420.2760.1520.1010.2290.0000.0000.0000.0000.1060.0160.1030.1300.2450.1960.2830.0000.1610.0430.1160.1980.1800.1260.0000.0000.0670.0180.0450.0310.0000.0000.0000.0000.0000.1070.0060.0810.0000.1180.0360.2120.0000.0320.0390.0000.0470.0250.0500.1870.1010.0130.0200.0540.0000.0220.0000.0770.2390.1190.0270.0670.0620.0690.0180.0000.1840.0060.0210.0430.0000.0000.0510.0000.0000.0000.2180.0380.0090.0000.0000.0220.0810.0000.0190.0520.0000.1010.1310.1660.0000.1700.0000.0000.0000.0000.0210.0660.0000.0000.0000.0580.0490.1620.1340.1500.0000.0000.1330.1810.0720.0340.2150.1580.1260.0000.1940.0000.0000.0620.2860.0270.1860.1490.0880.0000.0700.0910.1710.1160.2170.0430.0090.1980.0550.0540.0000.0010.0460.0000.1560.0000.0260.0970.0000.0160.0000.0000.0000.0210.0380.4440.2590.2420.0990.1700.2190.3120.0000.0380.1230.9941.0000.1720.3240.1390.0000.3380.0480.2910.1830.1120.0000.0000.0000.0330.0640.0000.0720.0400.2820.0000.0550.0000.2020.1370.1570.1510.0000.1440.0000.0760.3000.0000.1840.0000.1200.3340.0000.0000.0000.0630.0000.0490.0700.0000.0210.0180.0600.0000.0000.0000.0710.1080.108
Electrical_FuseA0.1760.0480.0000.2300.2230.3030.3460.1080.0000.0780.1000.0870.0890.0370.0900.1480.1630.2730.2040.1450.1020.1130.0000.0000.0000.0200.0920.0000.1980.1110.0810.1260.2200.0000.0930.0420.0610.1170.1180.0550.0000.0000.0060.0000.0310.0300.0000.0140.0000.0000.0000.1230.0000.0800.0000.0490.0500.1240.0000.0000.0890.0000.0470.0250.0490.1360.0000.0480.0190.0540.0000.0000.0000.0270.0410.0000.0270.0660.1210.0380.0000.0000.0000.0940.0210.0250.0000.0000.0000.0000.0000.0000.0980.0000.0100.1090.0000.0400.0710.0000.0510.0520.0000.0310.1240.1360.0000.0980.0000.0000.0560.0000.0390.0660.0000.0460.0000.0670.0000.0540.1260.1930.0000.0000.1070.1450.0500.0340.0700.1750.1720.0000.0710.0480.0000.0000.1380.0900.1900.0340.0000.0000.0580.0920.1860.1710.1530.0250.0100.0970.0630.0640.0140.0910.0710.0380.1360.0820.0620.0470.0000.0000.0000.0000.0000.0000.0000.0800.0000.0790.0000.0610.1330.0620.0000.0000.0940.1720.1721.0000.0000.0000.0000.8480.0190.1180.1700.1440.0000.0000.0000.0000.0330.0000.0210.0410.1540.0500.0410.0000.1280.1220.0990.1600.0000.0970.0000.0000.1160.0000.0920.0000.0330.1320.0370.0000.0000.0000.0000.0000.0690.0000.0190.0720.0000.0000.0000.0000.0710.0700.070
Electrical_FuseF0.1360.0000.0000.2320.2140.1950.2660.0750.0000.0000.1020.0110.0000.0000.0000.0880.0500.1770.1300.0000.0000.1040.0000.0000.0000.0000.0450.0000.0570.0390.1090.0740.1140.0000.0000.0000.0000.0230.0420.0520.0000.0000.0570.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0280.0000.1310.0000.0870.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0650.0000.0160.0720.1140.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0580.0000.0000.0000.0260.0000.0000.0130.0000.1160.0700.0790.0000.0000.0000.0080.0000.0000.0000.0220.0000.0000.0000.0250.1200.0730.0680.0670.0000.0000.0000.0390.0210.0000.0590.0880.0770.0000.1070.0270.0000.0000.0770.0000.0830.1190.0000.0000.0180.0000.0770.0380.0420.0000.0000.0930.0000.0230.0000.0000.0000.0000.0770.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.1680.0470.0970.0000.1360.0790.1270.0000.0900.0300.3240.3240.0001.0000.0000.0000.4370.0070.1800.0820.0300.0000.0000.0200.0000.0000.0000.0200.0000.1170.0000.0000.0000.1040.0530.0780.0920.0000.0700.0000.0000.0930.0000.0560.0000.0080.1030.0000.0000.0000.0000.0000.0000.0190.0000.0170.0000.0320.0000.0000.0000.0200.0620.062
Electrical_FuseP0.0920.0000.0000.0000.0000.0690.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.2250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1290.0160.0250.0260.0000.0000.0000.0180.0360.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0670.0000.0000.0000.0000.0000.0000.0000.0320.0380.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.1120.0000.0000.1390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0310.0000.0000.1390.1390.0000.0001.0000.0000.1180.0000.1310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1630.0540.0000.0000.0000.1030.0590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1400.0000.0000.0000.0000.0000.000
Electrical_Mix0.0000.0000.0000.0000.4410.0370.0000.0000.0000.0510.0000.0000.0000.0000.0000.0000.0000.1530.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0880.0000.0000.0150.0230.0000.0000.0000.0000.0000.0000.0770.0000.0000.0000.0000.3520.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0280.0000.0000.0000.0000.0000.2210.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2870.0220.0000.0000.0000.1860.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.000
Electrical_SBrkr0.2240.0430.0000.2990.2850.3690.4360.1510.0000.0980.1550.1210.0870.0000.1150.1640.1390.3330.2530.1650.1070.1590.0000.0000.0000.0000.1220.0000.2030.1270.1390.1630.2600.0000.0730.0540.0680.1340.1460.0930.0000.0000.0560.0000.0490.0420.0000.0330.0000.0000.0000.1280.0000.0970.0000.1220.0630.1740.0000.0260.0630.0000.0590.0360.0620.1410.0350.0600.0360.0670.0170.0000.0000.0000.0590.0610.0380.0810.1420.1040.0000.0000.0350.1130.0330.0000.0000.0140.0000.0000.0000.0000.1180.0000.0840.1070.0000.0380.0890.0000.0520.0680.0000.0970.1510.1640.0000.0970.0000.0180.0350.0000.0370.0820.0000.0490.0000.0760.0360.0930.1520.2090.0220.0000.1130.1610.0690.0460.1040.2080.2010.0000.1590.0690.0000.0000.1760.0680.2110.1010.0000.0000.0750.1330.2100.1620.1620.0410.0840.1480.0740.0810.0300.0840.0810.0280.1680.0750.0630.0670.0000.0000.0000.0220.0000.0220.0000.1600.0340.1350.0000.1450.1710.1230.0160.0280.1020.3380.3380.8480.4370.1180.0281.0000.0420.2280.1980.1440.0000.0360.0000.0000.0460.0000.0500.0270.2100.0300.0330.0000.1760.1390.1370.1930.0000.1250.0000.0620.1720.0000.1160.0000.1350.1920.0310.0000.0000.0000.0000.0000.0840.0000.0440.0700.0470.0000.0000.0000.0860.1010.101
KitchenQual_Ex0.1310.1820.0000.6510.1250.2590.2960.3320.0250.2460.4040.3590.1890.0000.3180.0810.2890.2860.3810.1370.1700.0510.0000.0770.0600.0000.1170.0000.1870.0980.0390.2260.4060.0280.0000.0000.0000.0730.0670.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0390.0230.0000.0340.0780.0000.0170.0000.4020.0000.0000.0500.0410.0320.1390.0190.0000.0000.0160.0360.0000.0270.0660.0000.0000.0000.0000.0320.0000.0000.0000.1860.0000.2030.0000.0000.0000.0000.0000.0000.0000.2200.1000.0000.0420.0310.0050.0000.0880.0530.0000.0000.0000.0000.0000.0000.2220.0870.0060.0390.0000.0610.0000.0000.0870.0490.0000.0000.0000.1670.2650.5550.0000.0700.2810.0000.0100.0310.0000.0490.0310.1730.2120.0000.0000.0000.5130.0000.0890.1720.0000.0300.0000.0000.1060.1130.0000.1270.0510.0830.2080.0180.0570.0000.0000.0000.0000.0000.0380.0520.0000.0000.0000.0000.0000.0000.2030.0340.0760.0000.1330.0480.0480.0190.0070.0000.0000.0421.0000.0250.2180.2690.0000.0000.0160.0000.0000.0000.0250.0000.0540.0000.1510.0000.1260.2800.0360.1790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0290.0000.0000.0000.0000.0000.2750.0000.1930.0000.0000.0000.0000.1860.2790.0550.055
KitchenQual_Fa0.1080.0000.0000.3340.2600.2570.2480.0930.0000.0000.1300.0690.0000.0790.0400.1960.1880.2140.2580.0710.0000.0810.0000.0000.0000.0410.0650.0000.1010.0690.0500.0820.2550.0000.0580.0000.0350.0590.0490.1400.0000.0000.1170.0000.0230.0000.0000.0190.0000.0000.0000.1710.0000.0420.0030.0590.0150.1190.0000.0000.0140.0000.0110.0000.0140.0000.1230.0000.0000.0190.0000.0000.0000.0000.1050.0000.0000.0310.0200.0000.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0670.0000.0440.0000.0000.0000.0450.0000.0170.0000.0000.0080.0490.0680.0000.0240.0000.0000.0000.0000.0000.0550.0000.0210.0000.0000.0000.0000.0560.0600.0000.0000.0730.1040.0350.0000.3100.1100.0490.0000.2390.0000.0720.0840.1040.0000.0700.0560.0000.0000.0330.0660.1000.0740.1280.0000.0440.1170.0310.0160.0000.0070.0110.0000.0970.0000.0000.0600.0000.0000.0000.0000.0000.0000.0000.1910.1130.2020.0000.0000.1010.1440.0000.0000.0500.2910.2910.1180.1800.1310.0000.2280.0251.0000.1290.1600.0420.0000.0000.0000.0380.0000.0400.0000.1510.0260.0200.0000.0530.0630.0880.0270.0000.0720.0000.0290.2300.0000.0350.0000.0760.2330.0000.0000.0000.0000.0000.0050.0330.0000.0210.0000.0180.0000.0000.0000.0340.0330.033
KitchenQual_Gd0.3250.0140.0000.5590.2910.5360.5590.0770.0740.2150.2550.2060.2440.0800.2800.0730.2130.5110.3230.2650.1930.1260.0630.0150.0000.0000.0780.0000.3990.1540.1550.1850.3690.0000.0530.2040.0460.0500.1510.0840.0000.0080.0130.0000.0550.0000.0000.0000.1100.0000.0610.1100.0000.2490.0000.1340.0680.0790.0640.0660.1870.0270.0160.1500.0320.1110.0080.1130.1160.2180.0530.0580.0000.0000.0930.1440.0000.1160.1310.0000.0200.0000.0170.1860.0500.0440.0000.0750.0000.0740.0000.0000.0610.0000.0000.0430.0000.0090.0680.0000.1290.0750.0000.0280.3640.1270.0000.0610.0000.0040.0000.0000.0000.0520.0000.1210.0000.1080.0250.0170.3610.1430.0000.0410.1150.1780.1210.0900.0690.6270.5560.0000.1060.0000.0000.0330.1430.3630.4770.0850.0000.0000.0000.1020.4480.3900.0980.0260.0000.0800.0930.0500.0370.1020.0970.1270.3130.0730.1720.0250.0000.0470.0000.0180.0280.1030.0000.0950.0540.0390.0000.0140.4130.1070.0420.0000.3650.1830.1830.1700.0820.0000.0000.1980.2180.1291.0000.8230.0000.0250.0620.0610.0410.0000.1110.0320.2500.0440.0790.0490.2040.1390.2640.2890.0000.1050.0320.0000.1840.0000.0930.0000.0390.2030.0590.0000.0030.0000.0000.0000.1470.0000.0870.0550.0140.0440.0000.0270.1440.1390.139
KitchenQual_TA0.3570.1150.0000.6100.3200.6110.6230.1850.0950.2380.3360.2790.2670.0640.3720.0960.2450.5950.3810.2790.2440.1230.0510.0220.0170.0710.1140.0000.4330.1790.1620.2310.4400.0000.0320.1860.0370.0690.1660.0290.0340.0000.0000.0000.0280.0000.0000.0000.1000.0000.0810.0670.0000.2110.0000.1170.0180.0540.0710.1020.2390.0450.0550.1480.2330.1180.0000.1490.0730.2240.1250.0700.0000.0000.0700.1630.0000.1190.1540.0000.0000.0000.0170.1930.0630.0740.0000.0000.0000.0000.0000.0000.0450.0000.0000.0490.0000.0720.1420.0000.1420.0970.0000.0000.3860.1300.0000.0580.0150.0000.0000.0000.0760.1240.0000.1320.0000.1320.0000.0000.3800.1470.0000.0620.0960.2260.2410.1730.0000.6160.6700.0000.0350.0080.0000.0320.1250.4510.5520.0760.0000.0150.2590.0750.3560.4450.0650.0480.0000.0400.1340.1010.0240.1580.1380.1660.3810.0850.1960.0370.0000.0580.0000.0510.0620.1310.0000.0320.0000.0000.0000.0290.4750.0780.0890.0000.4090.1120.1120.1440.0300.0000.0000.1440.2690.1600.8231.0000.0000.0380.0700.0810.0040.0000.1120.0180.2260.0240.1460.0640.2460.2570.2040.3630.0000.0670.0000.0000.1170.0000.0850.0000.0000.1450.0920.0000.0000.0000.0000.0000.2710.0150.1750.0570.0000.0460.0120.1240.2700.1540.154
Functional_Maj10.1410.0830.0000.2300.2810.0000.0000.0000.0900.0970.0000.1150.0000.0880.0970.0580.0880.0000.0430.1820.0000.0090.0000.0000.0000.0340.0000.0000.1140.0000.0000.0000.0670.0390.0000.0000.0000.0350.0620.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0290.0950.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.0000.0000.0830.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0510.0000.0500.0000.0950.0070.0380.0000.1110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0880.0000.0180.0000.0000.0420.0000.0000.0480.0000.0480.0000.0000.0490.0030.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0001.0000.0000.0000.0000.0000.0000.3480.0000.0480.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.000
Functional_Maj20.0000.0320.0000.0680.2090.0800.0000.0000.0260.0550.0000.0000.0000.1830.0000.0310.0000.0580.0000.0000.0000.1410.0000.0000.0000.0000.0000.0000.0470.0260.0000.0300.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0740.0000.0310.0000.0000.0000.0000.1030.0280.0000.0000.0000.1540.0280.0000.0000.0000.0160.0000.0000.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.0000.0170.0390.0000.0000.0000.0000.0000.0460.0000.0000.0000.0000.0000.0000.0000.2210.0360.0000.0000.0250.0380.0001.0000.0000.0000.0000.0000.1910.0000.0240.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.1240.0280.0000.0000.0000.0760.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Functional_Min10.0690.0000.1010.0950.0000.1210.1080.0000.1420.0400.0000.0000.0000.0000.0000.0000.0220.1340.0000.0740.0000.0000.0000.0000.0000.0000.0000.0000.0270.0540.0000.0080.0540.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0460.0160.0000.0330.0000.0000.0000.0000.0000.0000.0980.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0440.0000.0000.0560.0480.0000.0130.0000.0000.0000.0000.0530.0000.0000.0000.0000.0000.0000.0300.0000.0000.0900.0340.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0300.0000.0000.0990.0540.0000.0490.0160.0160.0000.0000.0000.0750.0770.0000.0000.0000.0000.0000.0000.0930.1140.0260.0000.0000.0000.0000.0580.0370.0000.0000.0000.0000.0330.0000.0000.0000.0000.0260.0620.0810.0090.0530.0000.0510.0000.0000.0530.1230.0000.0000.0000.0000.0000.0000.0740.0000.0160.0000.0380.0000.0000.0000.0200.0000.0000.0000.0160.0000.0620.0700.0000.0001.0000.0000.0000.0000.5330.0000.0000.0000.0080.0000.0000.0200.0250.0790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0260.0000.000
Functional_Min20.1230.0000.0000.1310.1260.1490.0760.0380.0680.0820.0720.0000.0450.0600.0210.0590.0000.1150.0000.0780.0000.0000.0320.0000.0000.0790.0000.0510.0000.0080.0000.0000.0400.0640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0910.0360.0000.0510.0060.0000.0000.0000.0220.0000.0000.0000.0020.0790.0190.0950.0000.0120.0000.0000.0000.0270.0000.0000.0000.0250.0990.0000.0520.0000.0000.0000.0000.0000.0000.0190.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0560.0160.0000.0000.0000.0000.0000.0000.0400.0000.0000.0000.0000.0000.0160.0740.0600.0000.0000.0730.0830.0000.0000.0000.0710.0740.0000.0000.0000.0000.0000.0180.0460.0930.0210.0000.0000.0280.0430.0770.0620.0590.0000.0000.0550.0000.0000.0250.0000.0000.0000.0790.0000.0000.0000.0000.0000.0000.0260.0000.0420.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0330.0330.0000.0000.0000.0000.0000.0000.0000.0610.0810.0000.0000.0001.0000.0000.0000.5600.0000.0460.0330.0000.0000.0000.0120.0210.0530.0000.0550.0000.0000.0370.0000.0430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0300.0000.0000.0000.0000.0340.0290.0000.000
Functional_Mod0.0720.0000.0690.0000.1750.0670.0000.0000.0770.0240.0630.0440.0610.0000.0000.0250.0240.0000.0000.0000.1400.0000.0000.1910.0000.0510.0000.0000.0000.0000.0620.0000.0000.0830.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0490.0000.0000.0000.0590.0000.0000.0000.0000.0160.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0840.0260.0000.0000.0210.0000.0210.0000.0000.0000.0000.0070.0000.0000.0000.0330.0000.0000.0000.0180.0000.0190.0000.0390.0000.0000.0000.0130.0860.0000.0000.0250.0000.0000.0000.0250.0000.0000.1060.0000.1240.0490.0000.0070.0500.1180.0000.0000.0000.0000.0000.0000.0760.0000.0000.0850.0210.0000.0000.0000.0000.0000.0510.0470.0000.0000.0000.0000.0000.0000.0000.0390.0000.0480.0000.0000.0000.0540.0000.0000.0250.0000.0000.0640.0640.0330.0000.0000.0000.0460.0000.0380.0410.0040.0000.0000.0000.0001.0000.0000.3610.0000.0400.0000.0000.0000.0290.0000.0510.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Functional_Sev0.0000.0000.0000.0000.0000.0000.0540.0000.2160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1840.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.1240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0590.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0360.0000.0000.0000.0000.1630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.000
Functional_Typ0.1380.0000.0720.1820.2410.1990.1120.0060.1250.1030.0730.0710.0000.0600.0000.0090.0250.1610.0310.1450.0650.0150.0000.0500.0000.0000.0000.0000.0000.0610.0160.0470.0760.0370.0000.0450.0000.0000.0600.0130.0000.0240.0140.0000.0000.0030.0000.0000.0000.0000.0000.0230.0870.0830.0000.0660.0530.0000.0000.0000.0380.0000.0000.0270.0520.0630.0000.0620.0230.0560.0000.0000.0000.0000.0000.0150.0000.0000.1020.0000.0000.0000.0000.0460.0000.0000.1010.0260.0000.0000.0000.0000.0000.0000.0960.0380.0000.0000.0000.0000.0000.0000.0000.0000.1030.0850.0240.0000.0000.0300.0000.0000.0240.0000.0000.0000.0000.0350.0000.0240.1230.0920.0190.0610.1030.0860.0000.0000.0660.1000.0880.0000.1070.0000.0360.0080.0530.0950.1660.1000.0000.0000.0510.0870.0940.0600.0810.0000.0960.0980.0520.0000.0350.0240.0000.0150.1490.1010.0320.0000.0000.0530.0340.0000.0480.1430.0000.0550.0490.0000.0000.0000.0790.0000.0130.0000.0480.0720.0720.0210.0200.0000.0360.0500.0250.0400.1110.1120.3480.1910.5330.5600.3610.0361.0000.0000.0850.0120.0000.0160.0690.0620.0820.1240.0000.0210.0000.0000.0400.0000.0270.0000.0000.0310.0000.0000.0000.0000.0000.0000.0720.0000.0380.0000.0000.0000.0000.0460.0740.0000.000
GarageType_2Types0.0990.0000.1470.0000.1930.0520.0710.0190.0000.1140.0000.0000.0000.0000.0000.0640.0000.0350.2820.0680.0210.0000.1090.0750.0000.0000.0000.0000.0460.0000.1340.0000.2040.0500.0000.0000.0360.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0730.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0090.0700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0290.0000.0000.0000.0000.0000.0000.0310.0380.0000.0000.0000.0000.0000.0360.0550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0120.0000.0240.0000.0000.0020.0000.0000.0000.0000.0000.0060.0320.0000.0000.0000.0200.0000.0000.0000.0000.0400.0400.0410.0000.0000.0000.0270.0000.0000.0320.0180.0000.0000.0000.0000.0000.0000.0001.0000.0620.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0270.027
GarageType_Attchd0.4100.1880.0000.3840.2520.5100.3560.2720.0400.1970.3900.3640.2950.1350.2760.2020.2500.3860.3780.2290.1880.1860.0000.0000.0000.0000.1960.0000.2860.0870.1780.2760.3610.0000.0890.0000.0770.3020.3020.1490.0110.0000.0650.0260.1140.0000.0000.0190.0790.0000.0910.2210.0640.1450.0540.1700.0300.1720.0000.0320.0450.0280.1580.0990.0740.2590.1420.0000.0610.0410.0420.0640.0580.0730.1030.1440.0970.1140.2270.0800.2130.0560.0940.0000.0620.0000.0000.0670.0940.0920.0000.0000.1100.0000.0000.0000.0000.0340.0940.0000.1920.0930.0000.1090.1870.1590.0460.1100.0000.0000.0100.0000.0480.0930.0000.1840.0000.0850.0000.1350.1870.1530.0940.0000.2330.2920.1070.0500.1090.2880.2790.0300.0900.0520.0000.1020.3460.0000.2570.0700.0000.0000.1310.1280.2620.2580.1290.0190.0000.1140.1090.1260.0000.1300.0000.0000.2660.0620.0390.1810.0290.0000.0000.0200.0000.0000.0000.1460.0880.0690.0000.0430.1880.1030.0550.0000.1090.2820.2820.1540.1170.0300.0000.2100.0540.1510.2500.2260.0480.0240.0000.0460.0400.0000.0850.0621.0000.1310.3040.0830.7270.2060.3660.3820.0300.1710.0000.0300.3370.0000.1650.0000.0690.3390.0000.0000.0000.0000.0530.0000.1120.0000.0670.0820.0430.0000.0000.0000.1120.1680.168
GarageType_Basment0.1400.0690.0000.0000.0520.0300.0700.0000.0420.0000.0780.0000.0000.0310.0990.0220.0480.0900.0180.0000.0000.0000.0000.0630.0000.0000.0000.0000.0000.0520.0000.0920.0340.0280.0070.0000.0000.0000.0000.0750.0000.0000.0440.0410.0000.0000.0000.0340.0000.0000.0000.0000.0000.0120.0540.1460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0190.0000.0340.0290.1640.0000.0000.0000.0000.0000.0000.0590.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0310.0180.0000.0590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0220.0000.0000.0000.0000.0000.0000.0000.0560.0530.0000.0000.0000.0000.0000.0000.0080.0240.0000.0000.0560.0000.0000.0000.0000.0000.0000.0000.0000.0080.0510.0000.0560.0090.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0500.0000.0000.0000.0300.0000.0260.0440.0240.0000.0000.0000.0330.0000.0000.0120.0000.1311.0000.0000.0000.0560.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.1020.0000.0000.0000.0000.0000.0000.0000.0240.0450.0000.0860.0000.0410.0000.0000.000
GarageType_BuiltIn0.3120.0390.0000.1930.1150.2390.1980.0990.0000.0920.0410.0760.4950.0000.3250.2230.3020.2300.1340.1360.0950.0520.0000.0000.0000.0410.0520.0000.2870.2540.0000.1710.1790.0000.0000.0000.0000.0740.0650.0190.0000.0000.0200.0000.0000.0070.0000.0000.0000.0000.0000.0130.0000.0000.0000.0120.2240.0180.0000.0000.0670.0000.0000.0000.1640.0510.0000.0000.0000.0000.0610.0000.0000.0470.0000.0000.0000.0510.0240.0000.2420.0000.0000.2590.0000.1030.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0480.0660.0000.0650.0480.0000.0000.1820.0600.0000.0000.0000.0000.0000.0000.0330.0600.0000.0800.0000.0630.0000.0000.1680.0560.0520.0000.0330.0930.0960.1270.0000.1380.1830.0000.0000.0180.0000.0380.0540.1410.1870.0000.0000.0000.1250.0150.1090.1640.0000.0250.0000.0000.0320.0000.0000.0000.0480.0550.0080.0450.0600.1340.0000.0120.0000.0270.0000.0590.0000.0100.0000.0000.0000.0000.1640.0290.0390.0000.1190.0550.0550.0410.0000.0000.0000.0330.1510.0200.0790.1460.0000.0000.0080.0000.0000.0000.0000.0000.3040.0001.0000.0000.1470.2500.0000.1790.0000.0280.0000.0000.0660.0000.0150.0000.0000.0710.0240.0000.0000.0000.0530.0000.1130.0000.0800.0130.0000.0000.0000.0430.1100.0940.094
GarageType_CarPort0.1670.0000.0000.1370.0240.0540.0480.0000.1320.0570.0000.0000.0000.0000.0000.1290.0000.0540.0000.0000.0000.0000.0000.0000.0000.0520.0650.0000.0960.0840.1460.0290.0000.0000.0380.0000.0210.0000.0000.0400.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0660.0000.0000.0070.0000.0000.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0660.0000.1480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0410.0160.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.0000.1190.0340.0000.0000.0000.1240.0100.0000.0000.0000.0000.0000.0000.0000.0380.0560.0890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0320.0000.0000.0000.0000.0260.0000.0470.0780.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0640.0000.0000.0000.0000.0000.1630.0160.0000.0830.0000.0001.0000.0270.0220.0310.0810.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0320.0000.0000.0000.0000.000
GarageType_Detchd0.3830.2030.0000.3490.2700.5200.3680.2210.0000.1490.3070.3160.2780.1260.2550.1840.2340.4260.3480.2550.1790.1950.0000.0250.0000.0300.1450.0000.3210.1430.0800.2810.3370.0000.0470.0190.0210.3200.3200.1100.0180.0160.0250.0240.1070.0000.0000.0260.0520.0000.1200.2220.0490.1110.0800.0690.1310.1330.0520.0490.0000.0000.1170.0940.1360.2920.1150.0000.0310.0000.0680.0790.0350.0260.0240.0590.0790.0630.2450.0720.0960.0120.1150.0710.0050.0760.0190.0630.0970.0790.0000.0000.0340.0000.0000.0000.0000.0950.0360.0000.2380.0670.0000.1150.2460.1670.0160.0500.0000.0000.0000.0000.1010.0430.0000.2430.0000.0750.0170.1340.2410.1720.0460.0000.2230.2970.1320.0990.0000.2810.3140.0000.0000.0770.0000.0880.3760.0410.2890.0280.0000.0000.1640.1420.2890.3100.1010.0240.0000.0720.1360.1650.0000.1700.0000.0000.2610.0950.0780.1040.0000.0000.0000.0000.0000.0270.0000.1360.1060.0000.0000.0680.2120.0780.0410.0000.1550.2020.2020.1280.1040.0000.0000.1760.1260.0530.2040.2460.0110.0170.0000.0000.0290.0000.0690.0040.7270.0560.1470.0271.0000.2850.3150.6080.0520.2050.0000.0520.0250.0310.2140.0000.1010.0190.0000.0000.0000.0000.0000.0000.1540.0000.1190.0230.0000.0000.0000.0760.1510.1890.189
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GarageQual_Gd0.0740.0000.0000.0000.0650.1600.0690.0000.0000.0000.0000.0000.0530.0000.0000.0000.0110.0000.0000.1440.1440.0000.1710.0470.2610.0000.0000.0000.0000.0000.0130.0710.0310.0540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0000.0000.0000.0030.0410.0000.0190.0000.0000.0140.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0430.0160.0000.0000.0000.0000.0000.0000.0000.0430.0000.0000.0000.0000.0000.0000.0420.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0001.0000.0000.2790.0000.0000.3060.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.000
GarageQual_Po0.0640.0000.0000.0000.2540.1080.0700.0000.0000.0770.0000.0000.0000.0000.0000.0000.0000.2550.0000.0000.0000.1140.0000.0000.0000.0000.0000.0000.0000.0000.0450.0160.0580.0180.0000.0000.0000.0180.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0380.0000.0000.0000.0000.0680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1570.0000.0000.0450.0550.0000.0000.0000.1120.0000.0000.1390.0000.0000.0000.0000.2010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0360.0000.1030.0000.0000.0160.0210.0310.0000.0000.0760.0760.0000.0000.1630.2870.0620.0000.0290.0000.0000.0000.1240.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0520.0000.0000.0280.0000.0000.0001.0000.1060.0000.0000.0000.5440.1140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
GarageQual_TA0.2530.0790.0000.2980.2290.4210.2190.1500.0340.1230.1680.1540.0000.1450.2440.1720.1660.5070.7300.1370.1410.1800.0000.0720.0610.0000.1400.0690.1560.1430.2030.1690.7270.0330.0000.0620.0570.1750.2160.0740.0190.0000.0190.0000.0390.0000.0000.0000.0000.0000.0000.1080.0000.0840.0110.1530.0710.0930.0520.0000.0800.0000.0440.0260.0700.2520.0640.0170.0120.0750.0000.0210.0000.0410.1440.0840.0000.0720.1060.1150.0400.0440.0560.0580.0290.0370.0000.0610.0000.0740.0000.0000.1430.0220.0000.0000.0000.0000.0510.0000.0180.0290.0000.0000.1300.1440.0410.1030.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0410.1260.1330.0440.0000.1650.2260.0970.0380.1150.1720.1610.0540.1400.0000.0220.0740.2820.0000.1920.0000.0000.0000.0850.1600.1520.1350.0870.0000.0750.0640.0650.0310.0000.0570.0000.0070.1330.0000.0000.1500.0000.0110.0000.0480.0000.0100.0000.1250.0220.1550.0750.0000.1340.1290.0540.0000.0350.3000.3000.1160.0930.0540.0220.1720.0000.2300.1840.1170.0000.0280.0000.0370.0000.0000.0400.0000.3370.0000.0660.0000.0250.1640.1760.0000.1060.5400.2790.1061.0000.0750.2340.0700.1880.7820.0000.0000.0000.0000.0000.0000.0680.0540.0480.0870.0870.0180.0000.0000.0700.1150.115
GarageCond_Ex0.0000.0000.0000.0000.0330.0000.0350.0000.0000.0000.0000.0000.0000.0000.1060.0000.0000.0000.0540.0000.0000.0000.0000.0000.0000.0510.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.6110.0000.0000.0000.0751.0000.0000.0000.0000.0800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
GarageCond_Fa0.0260.0000.0000.1930.1360.2690.1500.0730.0000.0680.0790.0850.0280.0570.0150.0000.0000.3640.1730.0790.0000.2020.0290.0000.0000.0550.0510.0000.1030.0430.0280.0760.1160.0000.0000.0000.0000.0960.1140.0000.0000.0000.0000.0410.0180.0000.0230.0000.0000.0000.0000.0000.0000.0160.0490.0270.0090.0700.0000.0000.0250.0000.0000.0000.0060.1960.0000.0000.0000.0140.0000.0000.0000.0070.0000.0000.0000.0270.0310.0000.0230.0000.0000.0000.0000.0000.0000.0000.0580.0000.0000.0000.1130.0000.0480.0000.0000.0000.0240.0000.0000.0000.0000.0170.0370.0530.0000.0730.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0130.0580.0340.0420.0000.0000.0850.1040.0000.0000.0000.0830.0960.0000.0540.0140.0000.0000.1920.0000.1050.0000.0000.0000.0290.1340.0990.0670.1640.0000.0000.0990.0540.0040.0930.0000.0000.0000.0800.0000.0000.0860.0000.0000.0000.0000.0000.0000.0770.0790.0350.0000.0480.0000.0600.0780.0000.0000.0000.1840.1840.0920.0560.0000.0000.1160.0000.0350.0930.0850.0000.0000.0000.0430.0000.0000.0270.0000.1650.0000.0150.0000.2140.0680.0910.1710.0000.4600.0000.0000.2340.0001.0000.0000.0000.4850.0000.0000.0000.0000.0000.0130.0290.0000.0000.0000.0000.0000.0000.0190.0300.0630.063
GarageCond_Gd0.1050.0000.0000.0000.0960.1060.0990.0000.0000.0000.0000.0000.0000.1250.0000.0220.0000.1350.0050.1310.0000.0000.0000.0830.3280.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0390.0290.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0720.0000.0000.0000.0000.0000.0410.0160.0000.0000.0000.0000.0000.0000.0000.0770.0000.0000.0000.0000.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.0270.0000.0100.0010.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.3060.0000.0700.0000.0001.0000.0000.2310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.0000.0000.0000.0000.000
GarageCond_Po0.0000.0000.0000.0000.1560.2120.0900.0000.0000.0880.0000.0000.0000.0000.0000.0000.0490.3420.0380.0000.0000.1360.0000.0000.0000.0000.0000.0000.0000.0390.2640.0000.0810.0000.0000.0000.0000.0410.0630.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1430.0290.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.1170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0400.0000.0000.0000.0280.0350.0000.0950.0000.0000.0000.0890.0160.0180.0000.0680.0000.0000.1490.0420.0000.0000.0000.1290.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0930.0000.0000.0000.0000.0000.0000.0000.0870.0000.2090.0000.0000.0550.0650.0000.0000.0000.1200.1200.0330.0080.1030.1860.1350.0000.0760.0390.0000.0000.0760.0000.0000.0000.0000.0000.0000.0690.0000.0000.0000.1010.0090.0210.0680.0000.1800.0000.5440.1880.0000.0000.0001.0000.2000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0330.033
GarageCond_TA0.2350.0850.0000.3490.2380.3800.2130.1260.0280.0840.1670.1820.0200.1680.2450.1410.1610.5100.7660.1440.0690.1780.0000.0430.0670.0000.1170.0670.1690.1590.2030.2140.7670.0000.0370.0570.0640.1440.1680.0610.0000.0000.0270.0000.0300.0000.0000.0000.0000.0000.0000.0960.0000.0760.0000.1890.0660.1500.0600.0000.0540.0000.0620.0190.0650.1770.0520.0240.0000.0700.0200.0140.0000.0480.1400.0950.0000.0750.0590.0990.0210.0500.0310.0460.0390.0110.0200.0000.0310.0450.0130.0000.1750.0260.0000.0000.0000.0000.0290.0000.0000.0160.0000.0000.1000.1030.0000.1340.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0270.0950.0750.0360.0000.1490.1970.0730.0480.1240.1910.1810.0000.2050.0000.0260.0840.1970.0420.1840.0340.0000.0000.0780.1420.1530.1260.1820.0000.0800.1450.0590.0170.0000.0150.0000.0000.1330.0000.0000.1220.0000.0000.0140.0000.0000.0000.0260.1360.0000.1650.0800.0000.1470.1430.0310.0000.0640.3340.3340.1320.1030.0590.0260.1920.0350.2330.2030.1450.0140.0330.0000.0000.0040.0000.0310.0000.3390.0000.0710.0000.0190.1580.1720.0410.0590.2530.0470.1140.7820.0800.4850.2310.2001.0000.0220.0000.0000.0430.0000.0330.0700.0590.0350.0820.0930.0000.0000.0000.0710.1030.103
SaleType_COD0.0820.0000.0000.0500.0330.1240.1720.0000.1560.0340.0000.0540.0000.0000.0000.0000.0000.1090.0000.0690.0000.0460.0000.0000.0000.0000.0000.0000.0390.0240.0000.0760.0410.0580.0530.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0300.0000.0000.0450.0000.0000.0200.0000.0000.0000.0730.0000.0570.0000.0180.0000.0000.0170.0000.0230.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0600.0000.0000.0520.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0410.0000.0000.0020.0000.0000.0220.0250.0410.0000.0870.0110.0000.0180.0000.0000.0000.0000.0000.0100.0000.0260.0000.0180.0000.0000.0840.0180.0000.0340.0000.0000.0190.0000.0000.0530.0690.0000.0000.0140.0000.0100.0000.1430.1320.0000.0000.0000.0370.0000.0570.0810.0000.0000.0000.0000.0370.0410.0000.0670.0220.0000.0650.0340.0250.0000.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.1050.0380.0000.0000.0840.0000.0000.0370.0000.0000.0000.0310.0290.0000.0590.0920.0000.0000.0000.0000.0000.0000.0000.0000.0000.1020.0240.0000.0000.0260.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0221.0000.0000.0000.0000.0000.0000.0370.0000.4400.3270.0000.0000.0000.1650.0380.0000.000
SaleType_CWD0.0160.0000.0000.0200.0000.0000.0500.0000.1680.0000.0000.0160.0000.0000.0000.0800.0000.0780.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0310.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.0100.0000.0240.0000.0260.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.1120.0000.0000.0000.0430.0050.0000.0000.000
SaleType_Con0.0310.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0160.0000.0000.0000.0670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1750.0000.0000.0320.0000.0410.0000.0200.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.1010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0620.0000.0000.0000.0000.0000.0000.0000.000
SaleType_ConLD0.1420.0000.0000.1220.1500.0000.0000.0000.0000.0000.0000.0000.0000.1250.0000.0000.0000.0000.1880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.0480.0000.0000.0380.0550.1500.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0660.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0400.1380.0000.0000.0000.0360.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1240.0000.0000.0000.1460.0000.0000.0270.0000.0000.0010.0000.0000.0000.0000.0000.0000.0030.0560.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0240.0630.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0430.0000.0000.0001.0000.0000.0000.0000.0000.1870.0150.0000.0000.0000.0000.0000.0000.000
SaleType_ConLI0.0000.0000.0000.0770.0000.0000.0000.0460.0000.0000.0000.0000.0640.0000.0000.1540.0990.0000.0000.0000.1120.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0530.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.1300.0000.0000.0000.0000.0000.0000.0000.000
SaleType_ConLw0.0000.0000.0000.2520.0810.0460.0090.0000.1570.0000.0000.0000.0000.0000.0000.0000.0000.0540.0000.0000.0000.1390.0000.0000.0000.0260.0000.0060.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0570.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.0230.0000.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0870.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0000.0000.0000.0070.0000.0170.0000.0760.0000.0000.0000.0000.0000.0000.0000.0490.0490.0000.0000.0000.0000.0000.0000.0050.0000.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0330.0000.0000.0000.0000.0001.0000.0000.0000.1300.0000.0000.0000.0000.0000.0000.0000.000
SaleType_New0.1560.1400.0000.3510.2390.4390.5640.1850.0620.2860.3030.2480.0830.0000.1880.0000.1700.4730.3220.0930.1870.0840.0000.0000.0260.1030.0000.0740.2440.0510.0730.1190.3650.1380.0000.1910.0000.0000.0990.0210.0820.0300.0000.0000.0000.0000.0000.0250.0660.0000.0000.0480.0200.0310.0260.0270.0470.0310.0000.0240.1230.0000.0580.0350.2760.0690.0150.0590.0340.2540.1000.0450.0000.0000.0000.0440.0370.0590.0960.0000.0630.0000.0000.0360.0000.0530.0000.0460.0000.0670.0000.0000.0000.0000.0000.0430.0000.1260.1220.0000.0780.0760.0000.0000.2760.1160.0000.0000.0000.0000.0150.0000.1280.1160.0000.0750.0290.0910.0000.0000.2770.1130.0000.0000.0000.1680.3210.2950.0000.2280.3320.0000.0200.0930.0000.1060.0840.2560.3250.0130.0000.0000.3800.0290.0750.2610.0390.0810.0000.0000.1430.0370.0000.1330.1070.0850.1260.0590.0870.1250.0000.0270.0000.0400.0460.1150.0000.0260.0000.0000.0000.0000.2540.0420.0730.0000.1900.0700.0700.0690.0190.0000.0000.0840.2750.0330.1470.2710.0000.0000.0250.0280.0000.0000.0720.0000.1120.0000.1130.0000.1540.2070.0790.2350.0000.0410.0000.0000.0680.0000.0290.0000.0000.0700.0370.0000.0000.0000.0000.0001.0000.0000.7700.0730.0000.0000.0000.6420.9820.0300.030
SaleType_Oth0.0420.0000.0000.0640.0860.0130.0540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.1010.0000.0000.0000.0000.0000.0000.0350.1400.0000.0000.0000.0450.0160.1120.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0540.0000.0000.0000.0000.0590.0000.0000.0000.0000.0000.0000.0001.0000.0900.1340.0000.0000.0000.0730.0000.0000.000
SaleType_WD0.1160.1120.0000.2480.2020.3060.4210.1450.0730.2120.2480.2260.0640.0000.1240.0000.1260.3380.2440.0180.1220.0250.0000.0000.0000.1120.0000.0430.1600.0000.0170.0780.2680.0640.0470.1440.0000.0000.0710.0100.0340.0140.0000.0000.0000.0000.0000.0000.0330.0000.0000.0580.0000.0000.0240.0000.0000.0000.0200.0210.0570.0000.0130.0540.2090.0320.0350.0720.0480.1970.0770.0360.0000.0000.0000.0000.0000.0500.0520.0100.0830.0000.0000.0000.0160.0300.0020.0250.0000.0540.0000.0000.0000.0000.0000.0370.0000.0810.0790.0000.0180.0450.0000.0000.1650.0820.0000.0000.0000.0000.0100.0000.0830.0640.0000.0080.0110.0650.0000.0000.1680.0760.0000.0000.0000.1280.2460.2240.0000.1480.2370.0000.0000.0680.0000.0560.0530.1380.1890.0050.0000.0000.2810.0100.0000.1580.0000.0400.0000.0000.0760.0210.0000.0830.0720.0770.0630.0000.0500.1060.0000.0290.0000.0320.0000.0740.0000.0000.0000.0000.0000.0000.1410.0000.0620.0000.0900.0210.0210.0190.0170.0000.0000.0440.1930.0210.0870.1750.0000.0000.0000.0300.0000.0000.0380.0000.0670.0240.0800.0000.1190.1300.0650.1680.0000.0350.0000.0000.0480.0000.0000.0000.0000.0350.4400.1120.0620.1870.1300.1300.7700.0901.0000.1340.0000.0000.0000.6310.7660.0000.000
SaleCondition_Abnorml0.0290.0260.0000.0950.1030.1020.1230.0000.0590.0580.0000.0000.0490.0620.0840.0910.1230.1300.0900.0000.0400.1460.0000.0430.0600.0650.0000.0200.0900.0450.0000.0890.1060.0690.1220.0000.0840.0540.0000.0110.0130.0000.0000.0520.0000.0060.0000.0180.0000.0000.0250.0000.0000.0550.0000.0000.0390.0620.0000.0000.0450.0000.0000.0000.0520.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0340.0000.0290.0000.0250.0000.0250.0000.0000.0420.0000.0030.0000.0000.0000.0000.0000.0300.0000.0960.0000.0440.0000.0000.0420.0000.0000.0000.0000.0000.0000.0040.0350.0000.0000.0000.0000.0530.0000.0180.0610.0000.0260.0440.0370.0000.0650.0780.0000.0000.0000.0000.0000.0160.0700.0840.0000.0000.0000.0400.0260.0730.0980.0000.0000.0030.0000.0000.0360.0000.0340.0000.0000.0430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0580.0000.0000.0000.0780.0180.0180.0720.0000.0000.0360.0700.0000.0000.0550.0570.0000.0000.0000.0000.0000.0360.0000.0000.0820.0450.0130.0150.0230.0560.0100.0250.0000.0200.0000.0000.0870.0000.0000.0000.0300.0820.3270.0000.0000.0150.0000.0000.0730.1340.1341.0000.0000.0000.0000.5790.0740.0000.000
SaleCondition_AdjLand0.1340.0000.0000.0000.0630.0500.0000.0000.0000.0000.0000.0000.0470.0000.0000.0810.0000.0850.1430.0000.0000.0000.0000.0000.0000.0000.0000.3510.0000.0000.5010.0320.1520.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.1650.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0920.0000.0000.0380.0000.0000.0000.0000.0000.0200.0000.0590.0000.0660.0000.0000.0000.0430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0050.0000.0000.0280.0600.0600.0000.0320.1400.0000.0470.0000.0180.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0870.0000.0000.0000.0000.0930.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0910.0000.0000.000
SaleCondition_Alloca0.2620.0000.0000.0000.0000.0350.1080.0690.0000.0320.0620.1350.0000.0000.0430.1630.0470.0460.0000.0850.0000.0000.0000.0000.2830.0660.3590.0000.2040.1610.2340.0640.1300.0000.0280.0000.0000.0000.0000.1460.0000.0000.1040.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.1130.0000.0000.0000.0000.0890.0000.2470.0000.0000.0000.0000.0000.0000.0000.0000.1030.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.1310.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.1080.0540.0000.0330.0000.0000.0110.0240.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.1350.0000.0000.0000.0000.0000.0340.0000.0240.0000.0780.0240.0000.0000.0810.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0650.0000.0150.0000.1130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.0460.0150.0000.0000.0000.0000.0000.0000.0000.0000.0860.0000.0320.0000.0000.0200.0480.0000.0000.0150.0000.0180.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.1830.0000.0140.014
SaleCondition_Family0.0390.0930.0000.0000.0000.0140.0450.0000.0000.0840.0000.0000.0000.0000.0000.1190.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.0580.0330.0000.0730.0400.0000.0310.0610.0000.0000.0000.0000.0320.0000.0030.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0070.0000.0000.0310.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0630.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0300.0420.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.0150.0000.0000.0000.0000.0000.0140.0130.0000.0000.0390.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0170.0000.0110.0000.0540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0140.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.2430.0000.0000.000
SaleCondition_Normal0.0680.1140.0000.1720.1830.2180.3400.1500.0090.1580.1790.1640.0540.0000.0970.0590.1110.2360.1820.0340.1180.0720.0000.0000.0850.1130.0980.0710.1440.0230.0650.0000.2200.1390.0760.1170.0100.0350.0400.0000.0480.0000.0220.0000.0000.0140.0000.0000.0000.0000.0000.0470.0000.0180.0000.0000.0000.0000.0000.0000.0580.0000.0140.0160.1390.0000.0000.0390.0000.1580.0490.0150.0160.0000.0000.0420.0000.0000.0390.0260.0600.0000.0160.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0520.0980.0000.0300.0000.0480.0000.1360.0580.0000.0000.0000.0000.0480.0000.0550.0860.0000.0230.0000.0000.0000.0000.1370.0840.0000.0180.0250.0890.1910.1630.0000.0870.1530.0000.0000.0510.0000.0510.0220.1240.1540.0000.0000.0000.2170.0000.0000.1080.0050.0630.0000.0000.0900.0000.0000.0800.0650.0790.0420.0290.0300.1120.0000.0130.0000.0000.0150.0570.0000.0300.0100.0000.0000.0000.1060.0000.0910.0000.0120.0000.0000.0000.0000.0000.0000.0000.1860.0000.0270.1240.0000.0000.0000.0340.0000.0000.0460.0000.0000.0410.0430.0000.0760.0890.0000.1130.0000.0000.0000.0000.0000.0000.0190.0000.0000.0000.1650.0050.0000.0000.0000.0000.6420.0730.6310.5790.0910.1830.2431.0000.6510.0000.000
SaleCondition_Partial0.1570.1400.0000.3460.2380.4340.5600.1880.0510.2860.3030.2470.0840.0000.1840.0140.1640.4680.3200.0850.1800.0860.0000.0000.0240.0930.0000.0740.2370.0430.0730.1100.3630.1300.0000.1870.0000.0000.0940.0230.0800.0000.0000.0000.0000.0000.0000.0230.0640.0000.0000.0490.0000.0270.0280.0290.0440.0320.0000.0260.1250.0000.0590.0360.2720.0600.0170.0600.0360.2600.0980.0430.0000.0000.0000.0450.0380.0560.0980.0000.0700.0000.0000.0290.0000.0540.0000.0480.0000.0630.0000.0000.0000.0000.0000.0440.0000.1230.1230.0000.0800.0780.0000.0000.2780.1100.0000.0000.0000.0000.0170.0000.1250.1180.0000.0780.0290.0930.0000.0000.2790.1070.0000.0000.0000.1650.3240.2900.0000.2250.3270.0000.0210.0940.0000.1070.0860.2500.3200.0150.0000.0000.3730.0300.0730.2550.0400.0780.0000.0000.1380.0440.0000.1320.1020.0870.1260.0600.0890.1250.0000.0000.0000.0400.0470.1100.0000.0270.0000.0000.0000.0000.2590.0430.0760.0000.1930.0710.0710.0710.0200.0000.0000.0860.2790.0340.1440.2700.0000.0000.0260.0290.0000.0000.0740.0000.1120.0000.1100.0000.1510.2010.0790.2290.0000.0420.0000.0000.0700.0000.0300.0000.0000.0710.0380.0000.0000.0000.0000.0000.9820.0000.7660.0740.0000.0000.0000.6511.0000.0290.029
LotShape1_Irr0.2450.1170.1280.2010.1160.2810.2150.1510.0250.0960.1690.1770.2150.0190.2070.1020.1190.2300.2150.1790.0830.1320.0000.0280.0000.0370.0670.0010.1700.1250.0880.2080.2100.0000.0270.0660.0190.2660.2390.0000.1400.0910.1380.0700.3350.0000.0000.2550.0710.0000.0680.0330.0700.0300.0370.0990.2160.0870.0710.0000.0480.0000.0170.1130.0210.1460.0680.0110.0000.0480.0990.1000.0690.1450.0730.0520.1080.0350.1080.0460.0000.0000.0000.0640.0000.0290.0670.0000.0320.0000.0000.0000.0530.0000.0000.0000.0000.0320.0630.0000.0920.0590.0140.0000.0850.0640.0190.0660.0000.0330.0000.0000.0290.0440.0000.0840.0000.1000.0000.0000.0790.0780.0410.0110.0240.0800.0740.0510.0000.1690.1840.0000.0540.0000.0000.0000.1250.0500.1450.0400.0000.0350.0900.0530.1770.1850.0510.0540.0000.0120.0560.1420.0310.1340.0000.0000.1110.0000.0370.0530.0180.0000.0000.0000.0000.0000.0000.0550.0300.0000.0000.0020.1080.0000.0270.0000.0750.1080.1080.0700.0620.0000.0000.1010.0550.0330.1390.1540.0000.0000.0000.0000.0000.0000.0000.0270.1680.0000.0940.0000.1890.1760.0870.2010.0000.0930.0000.0000.1150.0000.0630.0000.0330.1030.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0140.0000.0000.0291.0000.999
LotShape1_Reg0.2450.1170.1280.2010.1160.2810.2150.1510.0250.0960.1690.1770.2150.0190.2070.1020.1190.2300.2150.1790.0830.1320.0000.0280.0000.0370.0670.0010.1700.1250.0880.2080.2100.0000.0270.0660.0190.2660.2390.0000.1400.0910.1380.0700.3350.0000.0000.2550.0710.0000.0680.0330.0700.0300.0370.0990.2160.0870.0710.0000.0480.0000.0170.1130.0210.1460.0680.0110.0000.0480.0990.1000.0690.1450.0730.0520.1080.0350.1080.0460.0000.0000.0000.0640.0000.0290.0670.0000.0320.0000.0000.0000.0530.0000.0000.0000.0000.0320.0630.0000.0920.0590.0140.0000.0850.0640.0190.0660.0000.0330.0000.0000.0290.0440.0000.0840.0000.1000.0000.0000.0790.0780.0410.0110.0240.0800.0740.0510.0000.1690.1840.0000.0540.0000.0000.0000.1250.0500.1450.0400.0000.0350.0900.0530.1770.1850.0510.0540.0000.0120.0560.1420.0310.1340.0000.0000.1110.0000.0370.0530.0180.0000.0000.0000.0000.0000.0000.0550.0300.0000.0000.0020.1080.0000.0270.0000.0750.1080.1080.0700.0620.0000.0000.1010.0550.0330.1390.1540.0000.0000.0000.0000.0000.0000.0000.0270.1680.0000.0940.0000.1890.1760.0870.2010.0000.0930.0000.0000.1150.0000.0630.0000.0330.1030.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0140.0000.0000.0290.9991.000

Missing values

2023-05-18T15:06:13.248523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-18T15:06:13.850372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MSSubClassLotFrontageLotAreaOverallQualOverallCondYearBuiltYearRemodAddBsmtFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSF1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageYrBltGarageCarsGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMoSoldYrSoldMSZoning_C (all)MSZoning_FVMSZoning_RHMSZoning_RLMSZoning_RMLandContour_BnkLandContour_HLSLandContour_LowLandContour_LvlLotConfig_CornerLotConfig_CulDSacLotConfig_FR2LotConfig_FR3LotConfig_InsideNeighborhood_BlmngtnNeighborhood_BluesteNeighborhood_BrDaleNeighborhood_BrkSideNeighborhood_ClearCrNeighborhood_CollgCrNeighborhood_CrawforNeighborhood_EdwardsNeighborhood_GilbertNeighborhood_IDOTRRNeighborhood_MeadowVNeighborhood_MitchelNeighborhood_NAmesNeighborhood_NPkVillNeighborhood_NWAmesNeighborhood_NoRidgeNeighborhood_NridgHtNeighborhood_OldTownNeighborhood_SWISUNeighborhood_SawyerNeighborhood_SawyerWNeighborhood_SomerstNeighborhood_StoneBrNeighborhood_TimberNeighborhood_VeenkerBldgType_1FamBldgType_2fmConBldgType_DuplexBldgType_TwnhsBldgType_TwnhsEHouseStyle_1.5FinHouseStyle_1.5UnfHouseStyle_1StoryHouseStyle_2.5FinHouseStyle_2.5UnfHouseStyle_2StoryHouseStyle_SFoyerHouseStyle_SLvlRoofStyle_FlatRoofStyle_GableRoofStyle_GambrelRoofStyle_HipRoofStyle_MansardRoofStyle_ShedExterior1st_AsbShngExterior1st_AsphShnExterior1st_BrkCommExterior1st_BrkFaceExterior1st_CBlockExterior1st_CemntBdExterior1st_HdBoardExterior1st_ImStuccExterior1st_MetalSdExterior1st_PlywoodExterior1st_StoneExterior1st_StuccoExterior1st_VinylSdExterior1st_Wd SdngExterior1st_WdShingExterior2nd_AsbShngExterior2nd_AsphShnExterior2nd_Brk CmnExterior2nd_BrkFaceExterior2nd_CBlockExterior2nd_CmentBdExterior2nd_HdBoardExterior2nd_ImStuccExterior2nd_MetalSdExterior2nd_OtherExterior2nd_PlywoodExterior2nd_StoneExterior2nd_StuccoExterior2nd_VinylSdExterior2nd_Wd SdngExterior2nd_Wd ShngMasVnrType_BrkCmnMasVnrType_BrkFaceMasVnrType_NoneMasVnrType_StoneExterQual_ExExterQual_FaExterQual_GdExterQual_TAExterCond_ExExterCond_FaExterCond_GdExterCond_PoExterCond_TAFoundation_BrkTilFoundation_CBlockFoundation_PConcFoundation_SlabFoundation_StoneFoundation_WoodBsmtQual_ExBsmtQual_FaBsmtQual_GdBsmtQual_TABsmtCond_FaBsmtCond_GdBsmtCond_PoBsmtCond_TABsmtExposure_AvBsmtExposure_GdBsmtExposure_MnBsmtExposure_NoBsmtFinType1_ALQBsmtFinType1_BLQBsmtFinType1_GLQBsmtFinType1_LwQBsmtFinType1_RecBsmtFinType1_UnfBsmtFinType2_ALQBsmtFinType2_BLQBsmtFinType2_GLQBsmtFinType2_LwQBsmtFinType2_RecBsmtFinType2_UnfHeating_FloorHeating_GasAHeating_GasWHeating_GravHeating_OthWHeating_WallHeatingQC_ExHeatingQC_FaHeatingQC_GdHeatingQC_PoHeatingQC_TACentralAir_NCentralAir_YElectrical_FuseAElectrical_FuseFElectrical_FusePElectrical_MixElectrical_SBrkrKitchenQual_ExKitchenQual_FaKitchenQual_GdKitchenQual_TAFunctional_Maj1Functional_Maj2Functional_Min1Functional_Min2Functional_ModFunctional_SevFunctional_TypGarageType_2TypesGarageType_AttchdGarageType_BasmentGarageType_BuiltInGarageType_CarPortGarageType_DetchdGarageFinish_FinGarageFinish_RFnGarageFinish_UnfGarageQual_ExGarageQual_FaGarageQual_GdGarageQual_PoGarageQual_TAGarageCond_ExGarageCond_FaGarageCond_GdGarageCond_PoGarageCond_TASaleType_CODSaleType_CWDSaleType_ConSaleType_ConLDSaleType_ConLISaleType_ConLwSaleType_NewSaleType_OthSaleType_WDSaleCondition_AbnormlSaleCondition_AdjLandSaleCondition_AllocaSaleCondition_FamilySaleCondition_NormalSaleCondition_PartialLotShape1_IrrLotShape1_Reg
06065.00000084507520032003706015085685685401710102131802003.0254806100002200800010000100001000001000000000000000000010000000001000100000000000000001000000000000000100010000100000100100000100001000100100000000101000010000010000100100000001010000010000010000100000000100001001
12080.00000096006819761976978028412621262001262012031611976.02460298000005200700010000100100000000000000000000000000110000001000000100000000000010000000000000010000000001000010000101000000100001010010000000000101000010000010000100010000001010000010000010000100000000100001001
26068.000000112507520012002486043492092086601786102131612001.0260804200009200800010000100001000001000000000000000000010000000001000100000000000000001000000000000000100010000100000100100000100001001000100000000101000010000010000100100000001010000010000010000100000000100001010
37060.00000095507519151970216054075696175601717101031711998.036420352720002200600010000110000000000100000000000000000010000000001000100000000000000000100000000000000001001000010000110000000010100000110000000000101000000100010000100100000001000001001000010000100000000110000010
46084.000000142608520002000655049011451145105302198102141912000.0383619284000012200800010000100100000000000000000100000000010000000001000100000000000000001000000000000000100010000100000100100000100001100000100000000101000010000010000100100000001010000010000010000100000000100001010
55085.00000014115551993199573206479679656601362101111501993.02480403003200010200900010000100001000000000001000000000000010000100000000100000000000000001000000000000000100001000010000100000100100001000100100000000101000010000010000100010000001010000001000010000100000000100001010
62075.0000001008485200420051369031716861694001694102031712004.026362555700008200700010000100001000000000000000000000100010000001000000100000000000000001000000000000000100000100100000100100010000001100000100000000101000010000010000100100000001010000010000010000100000000100001001
76070.049958103827619731973859322161107110798302090102131721973.0248423520422800011200900010000110000000000000000001000000000010000000001000100000000001000000000000001000000000000100010000101000000100001001010000001000001000010000010000100010000001010000010000010000100000000100001010
85051.0000006120751931195000952952102275201774002022821931.024689002050004200800001000100001000000000000000001000000010000100000000100000001000000000000000000000000001001000010000110000000010001000100000100000101000000100010100000010010000000001001010000000100000000110000001
919050.0000007420561939195085101409911077001077101022521939.012050400001200800010000110000000100000000000000000000001000010000000100000000000010000000000000010000000001000010000110000000010001000100100000000101000010000010000100010000001010000010001000000100000000100001001
MSSubClassLotFrontageLotAreaOverallQualOverallCondYearBuiltYearRemodAddBsmtFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSF1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageYrBltGarageCarsGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMoSoldYrSoldMSZoning_C (all)MSZoning_FVMSZoning_RHMSZoning_RLMSZoning_RMLandContour_BnkLandContour_HLSLandContour_LowLandContour_LvlLotConfig_CornerLotConfig_CulDSacLotConfig_FR2LotConfig_FR3LotConfig_InsideNeighborhood_BlmngtnNeighborhood_BluesteNeighborhood_BrDaleNeighborhood_BrkSideNeighborhood_ClearCrNeighborhood_CollgCrNeighborhood_CrawforNeighborhood_EdwardsNeighborhood_GilbertNeighborhood_IDOTRRNeighborhood_MeadowVNeighborhood_MitchelNeighborhood_NAmesNeighborhood_NPkVillNeighborhood_NWAmesNeighborhood_NoRidgeNeighborhood_NridgHtNeighborhood_OldTownNeighborhood_SWISUNeighborhood_SawyerNeighborhood_SawyerWNeighborhood_SomerstNeighborhood_StoneBrNeighborhood_TimberNeighborhood_VeenkerBldgType_1FamBldgType_2fmConBldgType_DuplexBldgType_TwnhsBldgType_TwnhsEHouseStyle_1.5FinHouseStyle_1.5UnfHouseStyle_1StoryHouseStyle_2.5FinHouseStyle_2.5UnfHouseStyle_2StoryHouseStyle_SFoyerHouseStyle_SLvlRoofStyle_FlatRoofStyle_GableRoofStyle_GambrelRoofStyle_HipRoofStyle_MansardRoofStyle_ShedExterior1st_AsbShngExterior1st_AsphShnExterior1st_BrkCommExterior1st_BrkFaceExterior1st_CBlockExterior1st_CemntBdExterior1st_HdBoardExterior1st_ImStuccExterior1st_MetalSdExterior1st_PlywoodExterior1st_StoneExterior1st_StuccoExterior1st_VinylSdExterior1st_Wd SdngExterior1st_WdShingExterior2nd_AsbShngExterior2nd_AsphShnExterior2nd_Brk CmnExterior2nd_BrkFaceExterior2nd_CBlockExterior2nd_CmentBdExterior2nd_HdBoardExterior2nd_ImStuccExterior2nd_MetalSdExterior2nd_OtherExterior2nd_PlywoodExterior2nd_StoneExterior2nd_StuccoExterior2nd_VinylSdExterior2nd_Wd SdngExterior2nd_Wd ShngMasVnrType_BrkCmnMasVnrType_BrkFaceMasVnrType_NoneMasVnrType_StoneExterQual_ExExterQual_FaExterQual_GdExterQual_TAExterCond_ExExterCond_FaExterCond_GdExterCond_PoExterCond_TAFoundation_BrkTilFoundation_CBlockFoundation_PConcFoundation_SlabFoundation_StoneFoundation_WoodBsmtQual_ExBsmtQual_FaBsmtQual_GdBsmtQual_TABsmtCond_FaBsmtCond_GdBsmtCond_PoBsmtCond_TABsmtExposure_AvBsmtExposure_GdBsmtExposure_MnBsmtExposure_NoBsmtFinType1_ALQBsmtFinType1_BLQBsmtFinType1_GLQBsmtFinType1_LwQBsmtFinType1_RecBsmtFinType1_UnfBsmtFinType2_ALQBsmtFinType2_BLQBsmtFinType2_GLQBsmtFinType2_LwQBsmtFinType2_RecBsmtFinType2_UnfHeating_FloorHeating_GasAHeating_GasWHeating_GravHeating_OthWHeating_WallHeatingQC_ExHeatingQC_FaHeatingQC_GdHeatingQC_PoHeatingQC_TACentralAir_NCentralAir_YElectrical_FuseAElectrical_FuseFElectrical_FusePElectrical_MixElectrical_SBrkrKitchenQual_ExKitchenQual_FaKitchenQual_GdKitchenQual_TAFunctional_Maj1Functional_Maj2Functional_Min1Functional_Min2Functional_ModFunctional_SevFunctional_TypGarageType_2TypesGarageType_AttchdGarageType_BasmentGarageType_BuiltInGarageType_CarPortGarageType_DetchdGarageFinish_FinGarageFinish_RFnGarageFinish_UnfGarageQual_ExGarageQual_FaGarageQual_GdGarageQual_PoGarageQual_TAGarageCond_ExGarageCond_FaGarageCond_GdGarageCond_PoGarageCond_TASaleType_CODSaleType_CWDSaleType_ConSaleType_ConLDSaleType_ConLISaleType_ConLwSaleType_NewSaleType_OthSaleType_WDSaleCondition_AbnormlSaleCondition_AdjLandSaleCondition_AllocaSaleCondition_FamilySaleCondition_NormalSaleCondition_PartialLotShape1_IrrLotShape1_Reg
14509060.0900055197419740089689689689601792002242801978.50616400324500009200900010000100100000000000000100000000000000100000001000100000000000000001000000000000000100001000010000101000000100001000100000100000101000000001010000100010000001000000000000000000000000000100001001
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14532090.017217552006200600114011401140001140001031601978.50616400365600007200600010000100001000000000001000000000000010000001000000100000000000000001000000000000000100001000010000100100000100001000100000100000101000010000010000100010000001000000000000000000000000000110000001
14542062.075007520042005410081112211221001221102021602004.00000024000113000010200901000000100001000000000000000000000100010000001000000100000000000000001000000000000000100001000100000100100000100001000100100000000101000010000010000100100000001010000010000010000100000000100001001
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